Patent Description:
The present disclosure claims the benefit and priority of <CIT>.

The examples of the present disclosure relates to a method for determining quantization parameters in neural network and related product.

A neural network (NN) is a mathematical or computational model which imitates structures and functions of a biological neural network. By training sample data, the neural network continuously revises weights and thresholds of the network to reduce an error function along a direction of negative gradient and approach an expected output. The neural network is a widely used recognition and classification model, which is mostly used for function approximation, model recognition and classification, data compression, time series prediction, and the like.

In practical applications, the neural network usually uses data of 32Bit. The data in the existing neural network occupies a number of bits, which requires a large storage space and high processing bandwidth in spite of ensuring precision, thereby increasing the cost.

<NPL>, discloses a method for determining quantization parameters in neural networks.

The present disclosure provides a method for determining quantization parameters in neural network and related product to solve the above technical problem.

In the process of neural network operation, a quantization parameter is determined during quantization by using technical schemes in the present disclosure. The quantization parameter is used by an artificial intelligence processor to quantize data involved in the process of neural network operation and convert high-precision data into low-precision fixed-point data, which may reduce storage space of data involved in the process of neural network operation. For example, a conversion of float32 to fix8 may reduce a model parameter by four times. Smaller data storage space enables neural network deployment to occupy smaller space, thus on-chip memory of an artificial intelligence processor chip may store more data, which may reduce memory access data in the artificial intelligence processor chip and improve computing performance.

To describe technical schemes in examples of the present disclosure more clearly, accompanied drawings in examples of the present disclosure will be briefly described hereinafter. Apparently, the described accompanied drawings below merely show examples of the present disclosure and are not intended to be considered as limitations of the present disclosure.

Technical schemes in examples of the present disclosure will be described clearly and completely hereinafter with reference to the accompanied drawings in examples of the present disclosure. Apparently, the described examples are merely some rather than all examples of the present disclosure.

It should be understood that the terms such as "first", "second", "third", "fourth" and the like used in the specification, the claims, and the accompanied drawings of the present disclosure are used for distinguishing between different objects rather than describing a particular order. The terms "include" and "comprise" used in the specification and claims are intended to indicate existence of the described features, whole body, steps, operations, elements, and/or components, but do not exclude the existence or addition of one or more other features, whole body, steps, operations, elements, components, and/or collections thereof.

It should also be understood that the terms used in the specification of the present disclosure are merely intended to describe specific examples rather than to limit the present disclosure. As used in the specification and claims of the present disclosure, singular forms of "a", "one", and "the" are intended to include plural forms unless the context clearly indicates other circumstances. It should be further understood that the term "and/or" used in the specification and claims of the present disclosure refers to any combination and all possible combinations of one or more listed relevant items.

As used in the specification and claims of the present disclosure, the term "if" may be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, phrases such as "if. is determined" or "if [the described conditions or events] are detected" may be interpreted as "once. is determined", "in response to determining", "once [the described conditions or events] are detected", or "in response to detecting [the described conditions or events]".

Definitions of technical terms:
Floating-point number: According to the IEEE floating-point standard, a floating-point number is a number represented in a form of V = (-<NUM>) ^ sign * mantissa * <NUM> ^ E , in which "sign" refers to a sign bit ( <NUM> refers to a positive number and <NUM> refers to a negative number); E refers to an exponent, which means to weight a floating-point number and the weight is an Eth power of <NUM> (possibly a negative power); and mantissa refers to a mantissa, which is a binary fraction whose range is <NUM>~<NUM>-ε or <NUM>-ε. Representation of a floating-point number in a computer is divided into three fields, which are encoded separately:.

Fixed-point number: A fixed-point number consists of three parts: a shared exponent, a sign bit, and a mantissa, in which the shared exponent refers to that an exponent is shared within a set of real numbers that need to be quantized; the sign bit determines whether a fixed-point number is positive or negative; and the mantissa determines the number of valid digits of a fixed-point number, which is precision. Taking an <NUM>-bit fixed-point number as an example, the numerical computing method is as follows: <MAT>.

Binary fraction: Any decimal number can be represented by a formula Σj * <NUM>i. For example, a decimal number <NUM> can be represented by the formula <NUM> as follows: <NUM>=<NUM>*<NUM><NUM>+<NUM>*<NUM><NUM>+<NUM>*<NUM>-<NUM>+<NUM>*<NUM>-<NUM>, in which a left side of a decimal point is a positive power of <NUM>, and a right side of the decimal point is a negative power of <NUM>. Similarly, a binary fraction can also be represented in this way, in which the left side of the decimal point is a positive power of <NUM> and the right side of the decimal point is a negative power of <NUM>. For example, a decimal number <NUM> can be represented as <NUM>=<NUM>*<NUM><NUM>+<NUM>*<NUM><NUM>+<NUM>*<NUM><NUM>+<NUM>*<NUM>-<NUM>+<NUM>*<NUM>-<NUM>, so <NUM> can be represented as a binary fraction <NUM>.

Overflow: In a fixed-point computation unit, representation of a number has a certain range. In a computation process, if a size of a number exceeds the representation range of a fixed-point number, it is called "overflow".

KL divergence (Kullback - Leibler divergence): It is also known as relative entropy, information divergence, and information gain. KL divergence is an asymmetrical measure of difference between two probability distributions P and Q. KL divergence is used to measure the average number of extra bits required to encode samples from P by using encoding based on Q. Typically, P represents actual distribution of data, Q represents theoretical distribution of data, model distribution of data, or approximate distribution of P.

Data bit width: The number of bits used to represent data.

Quantization: a process of converting high-precision numbers represented by <NUM> bits or <NUM> bits into fixed-point numbers that occupy less memory space, which may cause certain loss in precision.

Specific descriptions of a method for determining quantization parameters in neural network and related product provided in examples of the present disclosure will be illustrated in detail with reference to the accompanied drawings.

A neural network (NN) is a mathematical model which imitates structures and functions of a biological neural network, and is computed by a large number of connected neurons. Therefore, a neural network is a computational model, which consists of a large number of connected nodes (or called "neurons"). Each node represents a specific output function called activation function. A connection between each two neurons represents a weighted value which passses through the connection signal, which is called a weight. The weight can be taken as memory of a neural network. An output of a neural network varies according to different connection methods between neurons, different weights, and different activation functions. A neuron is a basic unit of the neural network, which obtains a certain number of inputs and a bias. The certain number of inputs and the bias are multiplied by a weight when a signal (value) arrives. The connection refers to connecting one neuron to another neuron in another layer or the same layer, and the connection is accompanied by an associated weight. In addition, the bias is an extra input of the neuron, which is always <NUM> and has its own connection weight. This ensures that the neuron can be activated even if all inputs are empty (all <NUM>).

In applications, if no non-linear function is applied to the neuron in the neural network, the neural network is only a linear function and is not powerful than a single neuron. If an output result of a neural network is between <NUM> and <NUM>, for example, in a case of cat-dog identification, an output close to <NUM> can be regarded as a cat and an output close to <NUM> can be regarded as a dog, an activation function such as a sigmoid activation function is introduced into the neural network to realize the cat-dog identification. A return value of the activation function is a number between <NUM> and <NUM>. Therefore, the activation function is configured to introduce non-linearity into the neural network, which may narrow down the range of a neural network operation result. In fact, how the activation function is represented is not important, and what is important is to parameterize a non-linear function by some weights, thus the non-linear function may be changed by changing the weights.

<FIG> is a schematic structural diagram of a neural network. The neural network shown in <FIG> contains three layers: an input layer, a hidden layer, and an output layer. The hidden layer shown in <FIG> contains five layers. A leftmost layer in the neural network is called the input layer and a neuron in the input layer is called an input neuron. As a first layer in the neural network, the input layer receives input signals (values) and transmits the signals (values) to a next layer. The input layer generally does not perform operations on the input signals (values), and has no associated weight or bias. The neural network shown in <FIG> contains four input signals: x1, x2, x3, and x4.

The hidden layer includes neurons (nodes). The neural network shown in <FIG> contains five hidden layers. A first hidden layer contains four neurons (nodes), a second hidden layer contains five neurons, a third hidden layer contains six neurons, a fourth hidden layer contains four neurons, and a fifth hidden layer contains three neurons. Finally, the hidden layer transmits operation values of the neurons to the output layer. In the neural network shown in <FIG>, each of the neurons in the five hidden layers is fully connected, and each of the neurons in each hidden layer is connected with each neuron in the next layer. It should be noted that in some neural networks, hidden layers may not be fully connected.

A rightmost layer of the neural network shown in <FIG> is called the output layer, and the neuron in the output layer is called an output neuron. The output layer receives the output from the last hidden layer. In the neural network shown in <FIG>, the output layer contains three neurons and three output signals (y1, y2, and y3).

In practical applications, plenty of sample data (including input and output) are given in advance to train an initial neural network. After training, a trained neural network is obtained, and the trained neural network may give a right output for the input in real environment in the future.

Before the discussion of neural network training, a loss function needs to be defined. A loss function is a function measuring performance of a neural network when the neural network performs a specific task. In some example, the loss function may be obtained as follows: transmitting each sample data along the neural network in the process of training a certain neural network to obtain an output value, performing subtraction on the output value and an expected value to obtain a difference, and then squaring the difference. The loss function obtained in the manner is the difference between the expected value and the true value. The purpose of training a neural network is to reduce the difference the value of the loss function. In some examples, the loss function can be represented as: <MAT>.

In the formula, y represents an expected value, ŷ represents an actual result obtained by each sample data in a sample data set transmitting through the neural network, i represents an index of each sample data in the sample data set, L(y,ŷ) represents the difference between the expected value y and the actual result ŷ, and m represents the number of sample data in the sample data set. Taking the cat-dog identification as an example, in a data set consisting of pictures of cats and dogs, a corresponding label of a picture of dog is <NUM> and a corresponding label of a picture of cat is <NUM>. The label corresponds to the expected value y in above formula. The purpose of transmitting each sample image to the neural network is to obtain a recognition result through the neural network. In order to calculate the loss function, each sample image in the sample data set must be traversed to obtain the actual result ŷ corresponding to each sample image, and then calculate the loss function according to the above definition. The value of the loss function being large means that the training of the neural network has not been finished and the weight needs to be adjusted.

At the beginning of neural network training, the weight needs to be initialized randomly. It is apparent that an initialized neural network may not provide a good result. In the training process, if start from an initialized neural network, a network with high precision may be obtained through training.

The training process of a neural network consists of two stages. The first stage is to perform a forward processing on a signal, which means to transmit the signal from the input layer to the output layer through the hidden layer. The second stage is to perform back propagation on a gradient, which means to propagate the gradient from the output layer to the hidden layer, and finally to the input layer, and sequentially adjust weights and biases of each layer in the neural network according to the gradient.

In the process of forward processing, an input value is input into the input layer in the neural network and an output (called a predicted value) is obtained from the output layer in the neural network. When the input value is input into the input layer in the neural network, the input layer does not perform any operation. In the hidden layer, the second hidden layer obtains a predicted intermediate result value from the first hidden layer to perform a computation operation and an activation operation, and then transmits the obtained predicted intermediate result value to the next hidden layer. The same operations are performed in the following layers to obtain the output value in the output layer in the neural network.

An output value called a predicted value is obtained after the forward processing. In order to calculate an error, the predicted value is compared with an actual output value to obtain a corresponding error. A chain rule of calculus is used in the back propagation. In the chain rule, derivatives of errors corresponding to the weights of the last layer in the neural network are calculated first. The derivatives are called gradients, which are then used to calculate the gradients of the penultimate layer in the neural network. The process is repeated until the gradient corresponding to each weight in the neural network is obtained. Finally, the corresponding gradient is subtracted from the each weight in the neural network, then the weight is updated once, which may reduce the value of errors.

For a neural network, fine-tuning refers to loading a trained neural network. The process of fine-tuning also consists of two stages, which is the same as that of training, which. The first stage is to perform the forward processing on a signal, and the second stage is to perform the back propagation on a gradient to update weights in the trained neural network. The difference between training and fine-tuning is that training refers to randomly processing an initialized neural network and starts from the beginning, while fine-tuning is not.

In the process of training or fine-tuning a neural network, weights in the neural network are updated once based on gradients every time the neural network performs a forward processing on a signal and performs a corresponding back propagation on an error, and the whole process is called an iteration. In order to obtain a neural network with expected precision, a large sample data set is needed in the training process, but it is impossible to input the sample data set into a computer at one time. Therefore, in order to solve the problem, the sample data set needs to be divided into multiple blocks and then each block of the sample data set is passed to the computer. After the forward processing is performed on the each block of the sample data set, the weights in the neural network are correspondingly updated once. When the neural network performs a forward processing on a complete sample data set and returns a weight update correspondingly, the process is called an epoch. In practice, it is not enough to transmit a complete data set in the neural network only once, therefore it is necessary to transmit the complete data set in the same neural network for multiple times, which means that multiple epochs are needed to obtain a neural network with expected precision.

In the process of training or fine-tuning a neural network, it is expected to have faster speed and higher precision. Since data in the neural network is represented in a high-precision data format such as floating-point numbers, all the data involved in the process of training or fine-tuning is in the high-precision data format and then the trained neural network is quantized. Taking a condition when quantized objects are weights of a whole neural network and the quantized weights are <NUM>-bit fixed-point numbers as an example, since a neural network usually contains millions of connections, almost all the space is occupied by weights that are connected with neurons. The weights are different floating-point numbers and weights of each layer tend to be normally distributed in a certain interval, such as (-<NUM>, <NUM>). A maximum value and a minimum value corresponding to the weights of each layer in the neural network are stored, and the value of each floating-point number is represented by an <NUM>-bit fixed-point number. The interval within the range of the maximum value and the minimum value is linearly divided into <NUM> quantization intervals, in which each quantization interval is represented by an <NUM>-bit fixed-point number. For example, in an interval of (- <NUM>, <NUM>), byte <NUM> represents - <NUM> and byte <NUM> represents <NUM>. Similarly, byte <NUM> represents <NUM>.

For data represented in a high-precision data format such as a floating-point number, based on rules of computation representation of floating-point and fixed-point numbers according to a computer architecture, for a fixed-point computation and a floating-point computation of a same length, a floating-point computation model is more complex and needs more logic devices to build a floating-point computation unit, which means that a volume of the floating-point computation unit is larger than the volume of a fixed-point computation unit. Moreover, the floating-point computation unit needs to consume more resources to process, so that a gap of power consumption between the fixed-point computation unit and the floating-point computation unit is usually an order of magnitude. The floating-point computation unit occupies many times more chip area and consumes many times more power than the fixed-point computation unit.

However, the floating-point computation is also irreplaceable. Firstly, although the fixed-point computation is straightforward, a fixed position of decimals determines an integer part and a decimal part with a fixed number of bits, which may be inconvenient to simultaneously represent a large number or a small number, and may lead to overflow.

In addition, when an artificial intelligence processor chip is used for training or fine-tuning, the floating-point computation unit may be more suitable than the fixed-point computation unit, which is mainly because in a neural network with supervised learning, only the floating-point computation unit is capable of recording and capturing tiny increments in training. Therefore, how computing capability of chip training can be improved without increasing the artificial intelligence chip area and power consumption is an urgent problem to be solved.

For those skilled in the art, based on practice, training with low bit-width fixed-point numbers requires fixed-point numbers greater than <NUM>-bit to perform the back propagation on gradients, which means that the process of training with low bit-width fixed-point numbers may be complex. Therefore, how a floating-point computation unit can be replaced with a fixed-point computation unit to achieve fast speed of the fixed-point computation and how peak computation power of an artificial intelligence processor chip can be improved while precision of floating-point computation is ensured are technical problems which may be solved in the specification.

Based on descriptions of the above technical problem, high tolerance for input noise is a feature of a neural network. When identifying an object in a picture, the neural network may be capable of ignoring primary noise and focusing on important similarities, which means that the neural network may be capable of taking the low-precision computation as a source of noise and still producing accurate prediction results in a numerical format that contains a little information. It is necessary to find a universal data representation to perform low-precision training or fine-tuning, which may be capable of not only reducing data overflow, but also making data near <NUM> within the target interval better represented. Therefore, the data representation needs to have adaptability to adjust with the training or fine-tuning process.

Based on the above description, <FIG> is a flow chart illustrating a method for determining quantization parameters in neural network according to an example of the present disclosure. The quantization parameter determined by the technical scheme shown in <FIG> is used for data representation of quantized data to determine quantized fixed-point numbers. The quantized fixed-point numbers are used for training, fine-tuning, or inference of a neural network. The method includes:
step <NUM>: obtaining an analyzing result of each type of data to be quantized, in which the data to be quantized includes at least one type of data among neurons, weights, gradients, and biases of the neural network.

As mentioned above, in the process of training or fine-tuning a neural network, each layer in the neural network includes four types of data: neurons, weights, gradients, and biases. In the inference process, each layer in the neural network includes three types of data: neurons, weights, and biases, which are all represented in the high-precision data format. The floating-point numbers are taken as an example of high-precision data in the specification. It should be made clear that the floating-point numbers is only a partial but not exhaustive list of examples. It should be noted that those of ordinary skill in the art may make modifications or variations, for example, high-precision data may be high bit-width fixed-point numbers with a wide range of representation, in which a lowest precision represented by the high bit-width fixed-point numbers is low enough, and the high bit-width fixed-point numbers may be converted into low bit-width fixed-point numbers by using the technical scheme in the present disclosure. However, as long as functions and technical effects realized by the modifications or variations are similar to those of the present disclosure, the modifications or variations shall fall within the scope of protection of the present disclosure.

No matter what a neural network structure it is, in the process of training or fine-tuning a neural network, the data to be quantized includes at least one type of data among neurons, weights, gradients, and biases of the neural network. In the inference process, the data to be quantized includes at least one type of data among neurons, weights, and biases of the neural network. If the data to be quantized are the weights, the data to be quantized may be all or part of the weights of a certain layer in the neural network. If the certain layer is a convolution layer, the data to be quantized may be all or part of the weights with a channel as a unit in the convolution layer, in which the channel refers to all or part of the channels of the convolution layer. It should be noted that only the convolution layer has a concept of channels. In the convolution layer, only the weights are quantized layer by layer in a channel manner.

The following example is that the data to be quantized are the neurons and the weights of a target layer in the neural network, and the technical scheme is described in detail below. In the step, the neurons and the weights of each layer in the target layer are analyzed respectively to obtain a maximum value and a minimum value of each type of the data to be quantized, and a maximum absolute value of each type of the data to be quantized may also be obtained. The target layer, as a layer needed to be quantized in the neural network, may be one layer or multiple layers. Taking one layer as a unit, the maximum absolute value of the data to be quantized may be determined by the maximum value and the minimum value of each type of the data to be quantized. The maximum absolute value of each type of the data to be quantized may be further obtained by calculating the absolute value of each type of the data to be quantized to obtain results and then traversing the results.

In practical applications, a reason why obtaining the maximum absolute value of each type of the data to be quantized according to the maximum value and the minimum value of each type of the data to be quantized is that, during quantization, the maximum value and the minimum value corresponding to the data to be quantized of each layer in the target layer are normally stored, which means that there is no need to consume more resources to calculate the absolute value of the data to be quantized and the maximum absolute value can be obtained directly based on the stored maximum and minimum value corresponding to the data to be quantized.

Step <NUM>: determining a corresponding quantization parameter by using the analyzing result of each type of the data to be quantized and data bit width, in which the quantization parameter is used by an artificial intelligence processor to perform corresponding quantization on data involved in a process of neural network operation.

In the step, the quantization parameter may include the following six situations, wherein situation three according to formula (<NUM>) is claimed, the remaining situations corresponding to non-claimed embodiments.

Situation one: the quantization parameter is a point position parameter s. In the situation, a following formula (<NUM>) may be used to quantize the data to be quantized to obtain quantized data Ix: <MAT>.

In the formula, s refers to the point position parameter; Ix refers to an n-bit binary representation value of data x after quantization; Fx refers to a floating-point value of the data x before quantization; and round refers to a rounding calculation, in which it should be noted that round is not limited to a round calculation and may refer to performing other rounding calculations such as a ceiling calculation, a flooring calculation, a fix calculation, and the like to replace the round calculation in formula (<NUM>). In the situation, a maximum value A of a floating-point number may be represented by an n-bit fixed-point number as <NUM>s(<NUM>n-<NUM>-<NUM>), then a maximum value in a number field of the data to be quantized may be represented by an n-bit fixed-point number as <NUM>s(<NUM>n-<NUM>-<NUM>) , and a minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as -<NUM>s(<NUM>n-<NUM>-<NUM>). The formula (<NUM>) shows that when the data to be quantized is quantized by using the quantization parameter corresponding to the first situation, a quantization interval is <NUM>s and is marked as C.

If Z is set to be a maximum absolute value of all floating-point numbers in the number field of the data to be quantized, Z needs to be included in A and greater than <MAT>, so a following formula (<NUM>) needs to be satisfied: <MAT> Therefore, <MAT>, then <MAT>, and <MAT>.

According to a formula (<NUM>), the n-bit binary representation value Ix of the data x after quantization is inversely quantized to obtain inverse quantized data Fx , in which the data format of the inverse quantized data Fx is the same as that of the corresponding data Fx before quantization, both of which are floating-point numbers.

Situation two: the quantization parameter is a first scaling coefficient f<NUM>. In the situation, a following formula (<NUM>) may be used to quantize the data to be quantized to obtain the quantized data Ix: <MAT>.

In the formula, f<NUM> refers to the first scaling coefficient; Ix refers to the n-bit binary representation value of the data x after quantization; Fx refers to the floating-point value of the data x before quantization; and round refers to the rounding calculation, in which it should be noted that round is not limited to the round calculation and may refer to performing other rounding calculations such as the ceiling calculation, the flooring calculation, the fix calculation, and the like to replace the round calculation in the formula (<NUM>). The formula (<NUM>) shows that when the data to be quantized is quantized with the quantization parameter corresponding to the second situation, the quantization interval is f<NUM> and is marked as C.

For the first scaling coefficient f<NUM> , a situation is that the point position parameter s is a known fixed value that does not change. Given <NUM>s = T , in which T is a fixed value, a maximum value A of a floating-point number may be represented by an n-bit fixed-point number as (<NUM>n-<NUM>-<NUM>)×T. In the situation, the maximum value A depends on data bit width n. Given Z is a maximum absolute value of all numbers in the number field of the data to be quantized, <MAT> and Z=(<NUM>n-<NUM>-<NUM>)× f<NUM>. The maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as (<NUM>n-<NUM>-<NUM>)×f<NUM> , and the minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as - (<NUM>n-<NUM>-<NUM>)×f<NUM>. In another situation, <NUM>s ×f<NUM> is considered as the first scaling coefficient f<NUM> as a whole in engineering applications, which means that the independent point position parameter s can be considered as not existing. In <NUM>s × f<NUM>, f<NUM> refers to a second scaling coefficient. Given Z to be the maximum absolute value of all numbers in the number field of the data to be quantized, then <MAT> and Z= (<NUM>n-<NUM>-<NUM>)×f<NUM>. The maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as (<NUM>n-<NUM>-<NUM>)×f<NUM>, and the minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as - (<NUM>n-<NUM>-<NUM>)×f<NUM>.

According to a formula (<NUM>), the n-bit binary representation value Ix of the data x after quantization is inversely quantized to obtain the inverse quantized data F̂x, in which the data format of the inverse quantized data F̂x is the same as that of the corresponding data Fx before quantization, both of which are floating-point numbers.

Situation three: the quantization parameter is the point position parameter s and the second scaling coefficient f<NUM>. In the situation, a following formula (<NUM>) may be used to quantize the data to be quantized to obtain the quantized data Ix.

In the formula, s refers to the point position parameter, f<NUM> refers to the second scaling coefficient, and <MAT>; Ix refers to the n-bit binary representation value of the data x after quantization; Fx refers to the floating-point value of the data x before quantization; and round refers to the rounding calculation, in which it should be noted that round is not limited to the round calculation and may refer to performing the ceiling calculation or the flooring calculation, to replace the round calculation in the formula (<NUM>). The maximum value A in the number field of the data to be quantized may be represented by an n-bit fixed-point number as <NUM>s (<NUM>n-<NUM>-<NUM>). The formula (<NUM>) shows that when the data to be quantized is quantized with the quantization parameter corresponding to the third situation, the quantization interval is <NUM>s × f<NUM> and is marked as C.

Given Z is the maximum absolute value of all numbers in the number field of the data to be quantized, according to the formula (<NUM>), <MAT> which means that <MAT> and <MAT>,.

When <MAT>, according to the formula (<NUM>), Z may not affect a precision representation. When f<NUM> =<NUM>, according to the formula (<NUM>) and formula (<NUM>), <MAT>. The maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as (<NUM>n-<NUM>-<NUM>) × <NUM>s × f<NUM>, and the minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as - (<NUM>n-<NUM> -<NUM>)× <NUM>s × f<NUM>.

<FIG> is a schematic diagram of symmetrical fixed-point data representation. The number field of the data to be quantized shown in <FIG> is distributed with "<NUM>" being a center of symmetry. Z refers to a maximum absolute value of all floating-point numbers in the number field of the data to be quantized, in <FIG>, A refers to a maximum value of a floating-point number that can be represented by an n-bit fixed-point number, and the floating-point number A is converted into a fixed-point number as <NUM>n-<NUM> — <NUM>. To avoid overflow, A needs to include Z. In practice, floating-point numbers involved in the process of neural network operation tend to be normally distributed in a certain interval, but may not be distributed with "<NUM>" being the center of symmetry. Therefore, the floating-point numbers being represented by fixed-point numbers may lead to overflow. To improve the situation, an offset is introduced into the quantization parameter, as shown in <FIG>. In <FIG>, the number field of the data to be quantized is not distributed with "<NUM>" being the center of symmetry. Zmin refers to the minimum value of all floating-point numbers in the number field of the data to be quantized and Zmax refers to the maximum value of all floating-point numbers in the number field of the data to be quantized. P is a center point between Zmin and Zmax. The whole number field of the data to be quantized is shifted to make the shifted number field of the data to be quantized distributed with "<NUM>" being the center of symmetry, and the maximum absolute value in the shifted number field of the data to be quantized is the maximum value. As shown in <FIG>, the offset refers to a horizontal distance between the point "<NUM>" and the point "P", and the distance is called an offset O , in which <MAT>, and <MAT>.

Based on the description of the offset O , a fourth situation of the quantization parameter appears, which is that the quantization parameter includes the point position parameter and the offset. In the situation, a following formula (<NUM>) may be used to quantize the data to be quantized to obtain the quantized data Ix.

In the formula, s refers to the point position parameter; O refers to the offset, and <MAT>; Ix refers to the n-bit binary representation value of the data x after quantization; Fx refers to the floating-point value of the data x before quantization; and round refers to the rounding calculation, in which it should be noted that round is not limited to the round calculation and may refer to performing other rounding calculations such as the ceiling calculation, the flooring calculation, the fix calculation, and the like to replace the round calculation in the formula (<NUM>). The maximum value A in the number field of the data to be quantized may be represented by an n-bit fixed-point number as <NUM>s (<NUM>n-<NUM>-<NUM>), then the maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as <NUM>s (<NUM>n-<NUM> -<NUM>)+O , and the minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as -<NUM>s (<NUM>n-<NUM>-<NUM>)+ O. The formula (<NUM>) shows that when the data to be quantized is quantized with the quantization parameter corresponding to the fourth situation, the quantization interval is <NUM>s and is marked as C.

Given Z is the maximum absolute value of all numbers in the number field of the data to be quantized and <MAT>, Z needs to be included in A and greater than <MAT>. According to the formula (<NUM>), <MAT> , then <MAT> and <MAT>.

Based on the description of the offset O, a fifth situation of the quantization parameter appears, which is that the quantization parameter includes the first scaling coefficient f<NUM> and the offset O. In the situation, a following formula (<NUM>) may be used to quantize the data to be quantized to obtain the quantized data Ix: <MAT>.

In the formula, f<NUM> refers to the first scaling coefficient; O refers to the offset;Ix refers to the n-bit binary representation value of the data x after quantization; Fx refers to the floating-point value of the data x before quantization; and round refers to the rounding calculation, in which it should be noted that round is not limited to the round calculation and may refer to performing other rounding calculations such as the ceiling calculation, the flooring calculation, the fix calculation, and the like to replace the round calculation in the formula (<NUM>). In one situation, the point position parameter s is a known fixed value that does not change. Given <NUM>s = T and T is a fixed value, the maximum value A of a floating-point number may be represented by an n-bit fixed-point number as (<NUM>n-<NUM> -<NUM>)× T. In the situation, the maximum value A depends on the data bit width n. Given Z to be a maximum absolute value of all numbers in the number field of the data to be quantized, then <MAT>f<NUM> and Z= (<NUM>n-<NUM> -<NUM>) × f<NUM>. The maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as (<NUM>n-<NUM> -<NUM>)× f<NUM>, and the minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as - (<NUM>n-<NUM>-<NUM>)× f<NUM>. In another situation, <NUM>s × f<NUM> is considered as the first scaling coefficient f<NUM> as a whole in engineering applications, which means that the independent point position parameter s can be considered as not existing. In <NUM>s × f<NUM> , f<NUM> refers to the second scaling coefficient. Given Z is the maximum absolute value of all numbers in the number field of the data to be quantized, <MAT> and Z=(<NUM>n-<NUM> - <NUM>) × f<NUM>. The maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as (<NUM>n-<NUM> -<NUM>)× f<NUM> +O , and the minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as - (<NUM>n-<NUM> -<NUM>) × f<NUM> +O.

The formula (<NUM>) shows that when the data to be quantized is quantized with the quantization parameter corresponding to the fifth situation, the quantization interval is f<NUM> and is marked as C.

Based on the description of the offset O, a sixth situation of the quantization parameter appears, which is that the quantization parameter includes the point position parameter, the second scaling coefficient f<NUM> , and the offset O. In the situation, a following formula (<NUM>) may be used to quantize the data to be quantized to obtain the quantized data Ix.

In the formula, s refers to the point position parameter; O refers to the offset; f<NUM> refers to the second scaling coefficient, and <MAT>; Ix refers to the n-bit binary representation value of the data x after quantization; Fx refers to the floating-point value of the data x before quantization; and round refers to the rounding calculation, in which it should be noted that round is not limited to the round calculation and may refer to performing other rounding calculations such as the ceiling calculation, the flooring calculation, the fix calculation, and the like to replace the round calculation in the formula (<NUM>). The maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as <NUM>s (<NUM>n-<NUM>-<NUM>). The formula (<NUM>) shows that when the data to be quantized is quantized with the quantization parameter corresponding to the sixth situation, the quantization interval is <NUM>s × f<NUM> and is marked as C.

Given Z is the maximum absolute value of all numbers in the number field of the data to be quantized, according to the formula (<NUM>), <MAT>.

When <MAT>, according to the formula (<NUM>), Z may not affect the precision representation. When f<NUM> =<NUM>, <MAT>. The maximum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as (<NUM>n-<NUM>-<NUM>)× <NUM>s × f<NUM> +O, and the minimum value in the number field of the data to be quantized may be represented by an n-bit fixed-point number as -(<NUM>n-<NUM> -<NUM>)× <NUM>s × f<NUM>+O.

The determination processes of six type of quantization parameters are described in detail above, and are merely exemplary descriptions. The types of quantization parameters can be different from the above description in different examples. According to the formula (<NUM>) to the formula (<NUM>), both the point position parameter and the scaling coefficients are related to the data bit width. Different data bit width may lead to different point position parameters and scaling coefficients, which may affect the quantization precision. In the process of training or fine-tuning, within a certain range of iteration times, quantization by using the same bit width may have little effect on the overall precision of the neural network operation. If the number of iterations exceeds a certain number, quantization by using the same bit width may not meet the training or fine-tuning requirements on precision, which requires adjustment of the data bit width n with the training or the fine-tuning process. Simply, the data bit width n can be set artificially. Within different ranges of iterations times, a preset corresponding bit width n may be called. However, the training process by using low bit-width fixed-point numbers is complex. Therefore, the adjusting method of artificially presetting the data bit width basically cannot meet the requirements of practical applications.

In the present technical scheme, the data bit width n is adjusted according to the quantization error diffbit. Furthermore, the quantization error diffbit is compared with a threshold to obtain a comparison result, in which the threshold includes a first threshold and a second threshold, and the first threshold is greater than the second threshold. The comparison result may include three situations. If the quantization error diffbit is greater than or equal to the first threshold (situation one), the data bit width can be increased. If the quantization error diffbit is less than or equal to the second threshold (situation two), the data bit width can be reduced. If the quantization error diffbit is between the first threshold and the second threshold (situation three), the data bit width remains unchanged. In practical applications, the first threshold and the second threshold may be empirical values or variable hyperparameters. Conventional optimization methods for hyperparameters are suitable for both the first threshold and the second threshold, which will not be described further.

It should be emphasized that the data bit width can be adjusted according to a fixed bit stride, or according to a variable stride based on a difference between the quantization error and an error threshold. The data bit width may be adjusted longer or shorter according to actual needs in the process of neural network operation. For example, the data bit width n in a current convolution layer is <NUM>, and is then adjusted to <NUM> according to the quantization error diffbit. In practical applications, the need for precision in the process of neural network operation can be met when the value of the data bit width n is <NUM>, but is not necessary to be <NUM>. In the way, the fixed-point computing speed may be greatly improved within a tolerance range of precision, which may improve a resource utilization rate of an artificial intelligence processor chip.

For the quantization error diffbit , the quantization error is determined according to the quantized data and the corresponding pre-quantized data. In practical applications, there are three quantization error determination methods, all of which can be applied to the present technical scheme. The first method is to determine the quantization error according to a formula (<NUM>) based on the quantization interval, the number of quantized data, and the corresponding pre-quantized data.

In the formula, C refers to the corresponding quantization interval during quantization, m refers to the number of quantized data obtained after quantization, and Fi refers to the corresponding floating-point value of the data to be quantized, in which i is a subscript of data in a set of the data to be quantized.

The second method is to determine the quantization error diffbit according to a formula (<NUM>) based on the quantized data and the corresponding inverse quantized data.

In the formula, Fi refers to the corresponding floating-point value of the data to be quantized, in which i is the subscript of data in the set of the data to be quantized. F̂i refers to the inverse quantized data corresponding to the floating-point value.

The third method is to determine the quantization error diffbit according to a formula (<NUM>) based on the quantized data and the corresponding inverse quantized data.

It should be emphasized that the above methods of obtaining the quantization error diffbit are only a partial but not exhaustive list of examples.

For the data bit width, <FIG> is a curve illustrating a weight variation range of a neural network in the training process. <FIG> is another curve illustrating a weight variation range of the neural network in the training process. In <FIG>, an abscissa represents the number of iterations, and an ordinate represents a maximum value of a weight after calculating a logarithm. The variation range curve of weight shown in <FIG> illustrates the weight variation situation of any convolution layer in the neural network corresponding to different iterations in the same epoch. In <FIG>, a conv0 layer corresponds to a weight variation range curve A; a conv1 layer corresponds to a weight variation range curve B; a conv2 layer corresponds to a weight variation range curve C; a conv3 layer corresponds to a weight variation range curve D; and the conv4 layer corresponds to the weight variation range curve e. According to <FIG>, in a same epoch, the variation range of the weight in each iteration is large in an initial stage of training, while in middle and later stages of training, the variation range of the weight in each iteration is not large. In the situation, in the middle and later stages of training, since the variation range of the weight is not large before and after each iteration, the weight of corresponding layers in each iteration have similarity within a certain iteration interval, and data involved in the neural network training process in each layer can be quantized by using the data bit width used in the quantization of the corresponding layer in the previous iteration. However, in the initial stage of training, because of the large variation range of the weight before and after each iteration, in order to realize the precision of the floating-point computation required for quantization, in each iteration in the initial stage of training, the weight of the corresponding layer in the current iteration is quantized by using the data bit width used in the quantization of the corresponding layer in the previous iteration, or the weight of the current layer is quantized based on the preset data bit width n of the current layer to obtain quantized fixed-point numbers. According to the quantized weight and the corresponding pre-quantized weight, the quantization error diffbit is determined. According to the comparison result of the quantization error diffbit and the threshold, the data bit width n used in the quantization of the corresponding layer in the previous iteration or the preset data bit width n of the current layer is adjusted, and the adjusted data bit width is applied to the quantization of the weight of the corresponding layer in the current iteration. Furthermore, in the process of training or fine-tuning, the weights between each layer in the neural network are independent of each other and have no similarity, which makes neurons between each layer independent of each other and have no similarity. Therefore, in the process of neural network training or fine-tuning, the data bit width of each layer in each iteration of the neural network is only suitable to be used in the corresponding neural network layer.

The weight is taken as an example above, in the process of neural network training or fine-tuning, the corresponding bit width of the neuron and the gradient are also the case, which will not be further described.

In the inference process of a neural network, the weights between each layer in the neural network are independent of each other and have no similarity, which makes neurons between each layer independent of each other and have no similarity. Therefore, in the inference process of the neural network, the data bit width of each layer in the neural network is applied to the corresponding layer. In practical applications, in the inference process, the input neuron of each layer may not be the same or similar. Moreover, since the weights between each layer in the neural network are independent of each other, the input neurons of each of the hidden layers in the neural network are different. During quantization, it may be not suitable for the data bit width used by the input neuron of the upper layer to be applied to the input neuron of the current layer. Therefore, in order to realize the precision of floating-point computation required for quantization, in the reference process, the input neuron of the current layer is quantized by using the data bit width used in the quantization of the upper layer, or the input neuron of the current layer is quantized based on the preset data bit width n of the current layer to obtain quantized fixed-point numbers. According to the pre-quantized input neuron and the corresponding quantized input neuron, the quantization error diffbit is determined. According to the comparison result of the quantization error diffbit and the threshold, the data bit width n used in the quantization of the upper layer or the preset data bit width n of the current layer is adjusted, and the adjusted data bit width is applied to the quantization of the input neuron of the corresponding layer in the current iteration. The corresponding data bit width of the weight is also the case, which will not be further described.

For the quantization parameter, it can be seen from <FIG> that in a same epoch, the variation range of the weight in each iteration is large in the initial stage of training, while in the middle and later stages of training, since the variation range of the weight is not large before and after each iteration, the weight of corresponding layers in each iteration have similarity within a certain iteration interval, which means that data involved in the neural network training process in each layer can be quantized by using the data bit width used in the quantization of the corresponding layer in the previous iteration. In the situation, in the middle and later stages of training, the quantization parameter may not need to be determined in each iteration and only determining the quantization parameter in each layer in each iteration of the neural network in the initial stage of training may still realize the precision of the floating-point computation required for quantization. Furthermore, in the process of training or fine-tuning, the weights between each layer in the neural network are independent of each other and have no similarity, which makes neuron between each layer independent of each other and have no similarity. Therefore, in the process of neural network training or fine-tuning, the data bit width of each layer in each iteration of the neural network is applied to the corresponding layer.

The weight is taken as an example above, in the process of neural network training or fine-tuning, the corresponding bit width of the neuron and the gradient are also the same case, which will not be further described.

In the inference process of a neural network, the weights of each layer in the neural network are independent of each other and have no similarity, which makes the neurons between each layer independent of each other and have no similarity. Therefore, in the inference process of the neural network, the quantitation parameter of each layer in the neural network is applied to the data to be quantized of the corresponding layer. For example, if the current layer of the neural network is a convolution layer and the quantization parameter of the data to be quantized of the current convolution layer is obtained according to the data to be quantized in the convolution layer based on the technical scheme shown in <FIG>, the quantization parameter can only be applied to the current convolution layer but not to other layers in the neural network, even if the other layers are convolution layers.

To sum up, an extension strategy of the data bit width and the quantization parameter is determined based on the similarity between data. If the similarity exists between data, the data bit width and the quantization parameter can be continuously used. If no similarity exists between data, the data bit width or the quantization parameter needs to be adjusted. The similarity between data is usually measured by KL divergence or by a following formula (<NUM>).

In some examples, if data A and data B meet the formula (<NUM>), the data A and the data B are determined to have similarity.

It should be noted that the above determination method of the quantization error, the adjusting method of the data bit width, and the extension strategy of the data bit width and the quantization parameter are only a partial but not exhaustive list of examples. For example, the above determination method of the quantization error, the adjusting method of the data bit width, and the extension strategy of the data bit width and the quantization parameter are all applicable to the fine-tuning process of a neural network. Moreover, for the measurement of similarity between data, the above-mentioned methods of measuring similarity by KL divergence and the formula (<NUM>) are only a partial but not exhaustive list of examples, such as a histogram matching method, a matrix decomposition method, an image similarity calculation method based on feature points, a proximity measurement standard method, and the like.

In summary, in the middle and later stages of training, since the variation range of the weight is not large before and after each iteration, the weights of the corresponding layer in each iteration have the similarity within a certain iteration interval. In order to make the technical scheme more universal in training or fine-tuning and realize reasonable unitization of the resources of the artificial intelligence processor chip, a strategy is needed to determine an iteration interval to make the data bit width n of the corresponding layer in each iteration remain unchanged within the iteration interval. If the iteration interval is exceeded, the data bit width n changes, then it is not necessary to determine in each iteration whether the data bit width n needs to be adjusted or not. The quantization parameter is also the case, which may improve the peak computing power of an artificial intelligence processor chip while simultaneously ensuring precision of floating-point computation required for quantization.

As shown in <FIG> is a flow chart illustrating a target iteration interval determination method. In the technical scheme shown in <FIG>, the target iteration interval includes at least one weight update iteration, and the same bit width is used in the quantization process within the same target iteration interval. The steps of determining the target iteration interval include:
step <NUM>: at a predicted time point, determining a variation trend value of a point position parameter corresponding to the data to be quantized in the weight iteration process, in which the predicted time point is configured to determine whether the data bit width needs to be adjusted or not, and the predicted time point corresponds to the time point when the weight update iteration is completed.

In the step, according to a formula (<NUM>), the variation trend value of the point position parameter is determined according to a moving average value of the point position parameter corresponding to a current predicted time point in the weight iteration process and a moving average value of the point position parameter corresponding to a previous predicted time point in the weight iteration process, or according to the point position parameter corresponding to the current predicted time point in the weight iteration process and the moving average value of the corresponding point position parameter corresponding to the previous predicted time point in the weight iteration process. A formula (<NUM>) is represented as: <MAT>.

In the formula (<NUM>), M refers to the moving average value of the point position parameter s , which increases with the training iteration, in which M(t) refers to the moving average value of the point position parameter s corresponding to the tth predicted time point, which increases with the training iteration and is obtained according to a formula (<NUM>); s(t) refers to the point position parameter s corresponding to the t Ih predicted time point; M(t-<NUM>) refers to the moving average value of the point position parameter s corresponding to the t-<NUM>th predicted time point; and α refers to a hyperparameter. diffupdate<NUM> measures the variation trend of the point position parameter s ,in which the variation of the point position parameter s is reflected in the variation of the maximum value Zmax of the current data to be quantized. A greater diffupdate<NUM> indicates a larger variation range of numerical values and requires an update frequency with a shorter interval, which means a smaller target iteration interval.

Step <NUM>: determining the corresponding target iteration interval according to the variation trend value of the point position parameter.

In the present technical scheme, the target iteration interval is determined according to a formula (<NUM>). For the target iteration interval, the same data bit width is used in the quantization process within the same target iteration interval, and the data bit width used in the quantization process within different target iteration intervals may be the same or different.

In the formula (<NUM>), I refers to the target iteration interval. diffupdate<NUM> refers to the variation trend value of the point position parameter. β and γ may be empirical values or variable hyperparameters. Conventional optimization methods for hyperparameters are suitable for both β and γ, which will not be described further.

In the present technical scheme, the predicted time point includes a first predicted time point, in which the first predicted time point is determined according to the target iteration interval. For example, the weight of the corresponding layer in the current iteration is quantized by using the data bit width used in the quantization of the corresponding layer in the previous iteration at the tth predicted time point in the training or fine-tuning process to obtain a quantized fixed-point number. The quantization error diffbit is determined according to the pre-quantized weight and the corresponding quantized weight. The quantization error diffbit is compared with the first threshold and the second threshold respectively to obtain a comparison result, and the comparison result is used to determine whether the data bit width used in the quantization of the corresponding layer in the previous iteration needs to be adjusted or not. If the tth first predicted time point corresponds to an <NUM>th iteration and the data bit width used in a 99th iteration is n<NUM>, the quantization error diffbit is determined according to the data bit width n<NUM> in the <NUM>th iteration, and then the quantization error diffbit is compared with the first threshold and the second threshold to obtain a comparison result. If it is determined according to the comparison result that the data bit width n<NUM> does not need to be adjusted, the target iteration interval is determined to be <NUM> iterations according to the formula (<NUM>). If the <NUM>th iteration is taken as an initial iteration within the current target iteration interval, the <NUM>th iteration to an <NUM>th iteration are taken as the current target iteration interval; and if the <NUM>th iteration is taken as a last iteration within the previous target iteration interval, an <NUM>st iteration to an <NUM>th iteration are taken as the current target iteration interval. During quantization within the current target iteration interval, the data bit width n<NUM> used in the previous target iteration interval is still used in each iteration. In the situation, the data bit widths used in quantization within different target iteration intervals can be the same. If the <NUM>th iteration to the <NUM>th iteration are taken as the current target iteration interval, the <NUM>th iteration in a next target iteration interval is taken as a t+<NUM>th first predicted time point; and if the <NUM>st iteration to the <NUM>th iteration are taken as the current target iteration interval, the <NUM>th iteration in the current target iteration interval is taken as the t+<NUM>th first predicted time point. At the t+<NUM>th first predicted time point, the quantization error diffbit is determined according to the data bit width n<NUM>, and the quantization error diffbit is compared with the first threshold and the second threshold to obtain a comparison result. It is determined according to the comparison result that the data bit width n<NUM> needs to be adjusted to n<NUM>, and the target iteration interval is determined to be <NUM> iterations according to the formula (<NUM>). Then the <NUM> iteration to an <NUM> iteration or an <NUM> iteration to the <NUM> iteration are taken as the target iteration interval, and the data bit width n<NUM> is used in each iteration during quantization within the target iteration interval. In the situation, the data bit widths used in quantization between different target iteration intervals can be different.

In the present technical scheme, no matter whether the first predicted time point is the initial iteration or the last iteration within the target iteration interval, the formula (<NUM>) is suitable to be used to obtain the variation trend value of the point position parameter. If the current first predicted time point is the initial iteration within the current target iteration interval, then in the formula (<NUM>), M(t) refers to the moving average value of the point position parameter s corresponding to the corresponding time point of the initial iteration within the current target iteration interval, which increases with the training iteration; s(t) refers to the point position parameter s corresponding to the corresponding time point of the initial iteration of the current target iteration interval; and M(t-<NUM>) refers to the moving average value of the point position parameter s corresponding to the corresponding time point of the initial iteration within the previous target iteration interval, which increases with the training iteration. If the current first predicted time point is the last iteration within the current target iteration interval, then in the formula (<NUM>), M(t) refers to the moving average value of the point position parameter s corresponding to the corresponding time point of the last iteration within the current target iteration interval, which increases with the training iteration; s(t) refers to the point position parameter s corresponding to the corresponding time point of the last iteration within the current target iteration interval; and M(t-<NUM>) refers to the moving average value of the point position parameter s corresponding to the corresponding time point of the last iteration within the previous target iteration interval, which increases with the training iteration.

In the present technical scheme, on the basis of including the first predicted time point, the predicted time point may further include a second predicted time point, in which the second predicted time point is determined according to a curve of data variation range. Based on the variation range of big data in the training process of a neural network, the curve of data variation range as shown in <FIG> is obtained.

Taking a weight as an example, it can be seen from the curve of data variation range shown in <FIG> that during the iteration interval period from the beginning of training to the Tth iteration, the data variation range is large in each weight update. During quantization at the current predicted time point, data is first quantized in the current iteration by using the data bit width n<NUM> used in the previous iteration, and then the corresponding quantization error is determined by the obtained quantization result and the corresponding pre-quantized data. The quantization error is compared with the first threshold and the second threshold respectively to obtain a comparison result, and the data bit width n<NUM> is adjusted according to the comparison result to obtain a data bit width n<NUM>. The data bit width n<NUM> is used to quantize the weight to be quantized involved in the current iteration. Then the target iteration interval is determined according to the formula (<NUM>) to determine a first predicted time point, and whether and how to adjust the data bit width are determined at the first predicted time point. Then a next target iteration interval is determined according to the formula (<NUM>) to obtain a next first predicted time point. During the iteration interval period from the beginning of training to the Tth iteration, the weight variation range is large before and after each iteration, which makes the weight of the corresponding layers in each iteration have no similarity. In order to ensure precision, during quantization, data of each layer in the current iteration may not continue to use the corresponding quantization parameter of the corresponding layer in the previous iteration. In the first T iterations, the data bit width can be adjusted by iterations. In the situation, the data bit width used by each iteration in the first T iterations is different, and the target iteration interval is one iteration. In order to optimize resource utilization of an artificial intelligence processor chip, the target iteration interval in the first T iterations can be preset according to laws revealed in the curve of data variation range shown in <FIG>, which means to directly preset the target iteration interval of the first T iterations according to the curve of data variation range without a need to use the formula (<NUM>) to determine the time point of weight update iteration completion corresponding to each iteration in the first T iterations as the second predicted time point. Therefore, the resources of the artificial intelligence processor chip may be utilized more reasonably. Form the curve of data variation range shown in <FIG>, the variation range is not large from the Tth iteration, so in the middle and later stages of training, it is not necessary to determine the quantization parameter in each iteration. In the Tth or the T+<NUM>th iteration, a quantization error is determined by using the pre-quantized data and the quantized data corresponding to the current iteration; whether and how to adjust the data bit width are determined by the quantization error; and the target iteration interval is determined according to the formula (<NUM>). If the target iteration interval is determined to be <NUM> iterations, it requires that the corresponding time point of <NUM> iterations after the Tth or the T+<NUM>th iteration be taken as the first predicted time point to determine whether and how to adjust the data bit width, and to determine the next target iteration interval according to the formula (<NUM>) so as to determine the next first predicted time point until the computation of all iterations within the same epoch is completed. On this basis, after each epoch, the data bit width or the quantization parameter may be adaptively adjust, and finally the quantized data may be used to obtain a neural network with an expected precision.

If a value of T is determined to be <NUM> according to the curve of weight variation range shown in <FIG> (the value does not correspond to <FIG>, it is only for convenience of description to assume that the value of T is <NUM>, and the value is not limited to the assumed value), an <NUM>th iteration in the training process is taken as the second predicted time point and the current first predicted time point is the <NUM>th iteration in the training process. The target iteration interval is determined to be <NUM> iterations according to the formula (<NUM>) in the <NUM>th iteration. Within the target iteration interval, when training to the <NUM>th iteration and reaching the second predicted time point, it is needed to determine whether and how to adjust the data bit width at the corresponding time point of the <NUM>th iteration, and to determine the target iteration interval according to the formula (<NUM>). If the target iteration interval in the situation is determined to be <NUM> iterations, the <NUM>th iteration to an <NUM>nd iteration are taken as the target iteration interval, and the <NUM>th iteration corresponding to the first predicted time point determined when the target iteration interval is <NUM> iterations is within the target iteration interval of <NUM> iterations. In the <NUM>th iteration, whether and how to adjust the data bit width can be determined according to formula (<NUM>). It is also possible to determine whether and how to adjust the data bit width directly in the <NUM>th iteration rather than in the <NUM>th iteration. In conclusion, whether to perform evaluation and prediction in the <NUM>th iteration or not are both suitable for the present technical scheme.

To sum up, the second predicted time point may be preset according to the curve of data variation range. In the initial stage of training or fine-tuning, it is not necessary to use resources of an artificial intelligence processor chip to determine a target iteration interval. At the preset second predicted time point, the data bit width is directly adjusted according to the quantization error, and the adjusted data is used to quantize the data to be quantized involved in the current iteration. In the middle and later stages of training or fine-tuning, the target iteration interval is obtained according to the formula (<NUM>) to determine the corresponding first predicted time point, and determine whether and how to adjust the data bit width at each first predicted time point. Therefore, resources of an artificial intelligence processor chip may be reasonably utilized while simultaneously ensuring the precision of floating-point computation required for quantization, which may improve a quantization efficiency.

In practice, in order to obtain a more accurate target iteration interval of data bit width, both a variation trend value diffupdate<NUM> of a point position parameter and a variation trend value diffupdate<NUM> of the data bit width can be considered simultaneously. As shown in <FIG> is another flow chart illustrating a target iteration interval determination method. The steps of determining the target iteration interval include:
step <NUM>: at a predicted time point, determining the variation trend value of the point position parameter and the variation trend value of the data bit width corresponding to the data to be quantized involved in the weight iteration process, in which the predicted time point is configured to determine whether the data bit width needs to be adjusted or not, and the predicted time point corresponds to the time point when the weight update iteration is completed.

It should be emphasized that the technical scheme shown in <FIG> for determining the target iteration interval of the data bit width based on the variation trend value of the point position parameter is applicable to the technical scheme shown in <FIG>, which will not be described further.

In the step, the variation trend value of the data bit width is determined by using the corresponding quantization error according to a formula (<NUM>).

In the formula (<NUM>), δ refers to a hyperparameter; diffbit refers to a quantization error; and diffupdate<NUM> refers to a variation trend value of data bit width. The diffupdate<NUM> measures the variation trend of the data bit width n used in quantization. A greater diffupdate<NUM> indicates that a fixed-point bit width needs to be updated and an update frequency with a shorter interval is needed.

The variation trend value of the point position parameter shown in <FIG> may still be obtained according to the formula (<NUM>), and M(t) in the formula (<NUM>) is obtained according to the formula (<NUM>). diffupdate<NUM> measures the variation trend of the point position parameter s ,in which the variation of the point position parameter s is reflected in the variation of the maximum value Zmax of the current data to be quantized. A greater diffupdate<NUM> indicates a larger variation range of numerical values and requires the update frequency with the shorter interval, which means a smaller target iteration interval.

Step <NUM>: determining the corresponding target iteration interval according to the variation trend value of the point position parameter and the variation trend value of the data bit width.

In the formula (<NUM>), I refers to the target iteration interval; β and γ refer to hyperparameters; diffupdate<NUM> refers to the variation trend value of the point position parameter; and diffupdate<NUM> refers to the variation trend value of the data bit width. β and γ may be empirical values or variable hyperparameters. Conventional optimization methods for hyperparameters are suitable for both β and γ, which will not be described further.

In the present technical scheme, diffupdate<NUM> measures the variation trend of the point position parameter s , but the variation of the point position parameter s caused by the variation of the data bit width n needs to be ignored because the variation of the data bit width n has been reflected in diffupdate<NUM>. If the variation of the point position parameter s caused by the variation of the data bit width n is not ignored, the target iteration interval I determined according to the formula (<NUM>) may be inaccurate, which may result in too many first predicted time points. As a result, in the process of training or fine-tuning, the operation of determining whether and how to update the data bit width n may be frequently performed, which may lead to unreasonable utilization of resources of an artificial intelligence processor chip.

Based on the above description, diffupdate<NUM> is determined according to M(t). If the data bit width corresponding to the T-<NUM>th predicted time point is n<NUM>, the moving average value of the point position parameter is m<NUM> , which increases with the training iteration. The data to be quantized is quantized by using the data bit width n<NUM> to obtain a quantized fixed-point number. The quantization error diffbit is determined according to the pre-quantized data and the corresponding quantized data, and the quantization error diffbit is compared with the threshold to obtain a comparison result. According to the comparison result, the data bit width n<NUM> is adjusted to n<NUM>, and the data bit width is adjusted by |n<NUM> - n<NUM>| bits. The data bit width used in quantization at the tth predicted time point is n<NUM>. In order to ignore the variation of the point position parameter caused by the variation of the data bit width, one of following two optimization methods can be selected when M(t) is determined. The first method: if the data bit width is increased by |n<NUM> - n<NUM>| bits, the value of s(t-<NUM>) is s<NUM>- |n<NUM> -n<NUM>| and the value of M(t-<NUM>) is m<NUM> - |n<NUM> - n<NUM>|, s(t-<NUM>) and M(t-<NUM>) are put into the formula (<NUM>) to obtain M(t) , which is the moving average value of the point position parameter corresponding to the tth predicted time point and increases with the training iteration. If the data bit width is reduced by |n<NUM> - n<NUM>| bits, the value of s(t-<NUM>) is s<NUM> + |n<NUM> - n<NUM>| and the value of M(t-<NUM>) is m<NUM> + |n<NUM> - n<NUM>|, s(t -<NUM>) and M(t-<NUM>) are put into the formula (<NUM>) to obtain M(t), which is the moving average value of the point position parameter corresponding to the tth predicted time point and increases with the training iteration. The second method: no matter whether the data bit width is increased or reduced by |n<NUM> - n<NUM>| bits, the value of s(t-<NUM>) is s<NUM> and the value of M(t-<NUM>) is m<NUM>, s(t-<NUM>) and M(t-<NUM>) are put into the formula (<NUM>) to obtain M(t). When the data bit width is increased by |n<NUM> -n<NUM>| bits, |n<NUM> -n<NUM>| is subtracted from M(t); and when the data bit width is reduced by |n<NUM> -n<NUM>| bits, |n<NUM> -n<NUM>| is added to M(t) ;the obtained result is taken as the moving average value of the point position parameter corresponding to the tth predicted time point, which increases with the training iteration. The above two methods are equivalent and can both ignore the variation of the point position parameter caused by the variation of the data bit width and obtain a more accurate target iteration interval, which may improve the resources utilization rate of an artificial intelligence processor chip.

In practical applications, the data bit width n and the point position parameter s may have a great impact on quantization precision, while the second scaling coefficient f<NUM> and the offset O may have little impact on quantization precision. For the first scaling coefficient f<NUM>, as mentioned above, in the second situation when <NUM>s × f<NUM> is taken as the first scaling coefficient as a whole, since the point position parameter s may have a great impact on quantization, the first scaling coefficient may have a great impact on quantization. Therefore, in the present technical scheme, , it is meaningful to determine the target iteration interval of the point position parameter no matter whether the data bit width n and the point position parameter s are adjusted or not. The idea of the technical scheme shown in <FIG> can be used to determine the target iteration interval of the point position parameter s. Therefore, a method for determining the target iteration interval of the point position parameter s shown in <FIG> includes:.

It should be emphasized that the technical scheme shown in <FIG> for determining the target iteration interval of the quantization parameter based on the variation trend value of the point position parameter is applicable to the technical scheme shown in <FIG>, which will not be described further. For the technical scheme shown in <FIG>, the quantization parameter is preferably a point position parameter.

It should be noted that the above determination methods of the target iteration interval of the data bit width and the target iteration interval of the quantization parameter are only a partial but not exhaustive list of examples. For example, the method of determining the target iteration interval of the quantization parameter after determining the target iteration interval of the data bit width is also suitable for the technical schemes shown in <FIG>.

The present technical scheme, which is used to determine a quantization parameter, adjust a data bit width or the quantization parameter according to a quantization error, and determine a target iteration interval which determines whether to adjust the data bit width or the quantization parameter, makes it possible to adjust the data bit width or the quantization parameter at an appropriate time point in the process of neural network operation, so as to use an appropriate quantization parameter at an appropriate iteration time point. Therefore, an artificial intelligence processor chip may achieve a speed of performing a fixed-point computation when performing a neural network operation, which may improve peak computation power of an artificial intelligence processor chip while simultaneously ensuring precision of floating-point computation required for computation.

It should be noted that, for the sake of a simple description, the above examples of methods are described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not intended to be limited by the described order of action, as according to the disclosure, certain steps may be performed in other orders or at the same time. Those skilled in the art should also be aware that the examples described in the specification are alternative examples and that the actions and modules involved may not be necessary for this disclosure.

It should be further noted that although each step in the flow charts of <FIG>, <FIG> is shown in an order indicated by arrows, the steps are not necessarily performed in the order indicated by the arrows. Unless explicitly stated in the present disclosure, there are no strict restrictions on the performing orders of the steps, and the steps can be performed in other orders. Moreover, at least some of the steps in <FIG>, <FIG> may include multiple sub-steps or stages, in which the multiple sub-steps or stages may not necessarily be completed at the same time but completed at different times, and may not necessarily be performed sequentially but performed alternately or by turns with other steps or sub-steps or at least part of stages.

As shown in <FIG> is a block diagram of hardware configuration of a device for determining quantization parameters in neural network according to an example of the present disclosure. In <FIG>, a device <NUM> for determining quantization parameters in neural network may include a processor <NUM> and a memory <NUM>. It should be noted that in <FIG>, only elements related to the present disclosure are shown in the device <NUM> for determining quantization parameters in neural network. Therefore, it is obvious to those skilled in the art that the device <NUM> for determining quantization parameters in neural network may further include common elements different from those shown in <FIG>, such as a fixed-point computation unit.

The device <NUM> for determining quantization parameters in neural network may correspond to a computing device with various processing functions, such as generating a neural network, training or learning a neural network, quantizing a floating-point neural network into a fixed-point neural network, or retraining a neural network. For example, the device <NUM> for determining quantization parameters in neural network may be implemented as various types of devices, such as a personal computer (PC), a server device, a mobile device, and the like.

The processor <NUM> is configured to control all functions of the device <NUM> for determining quantization parameters in neural network. For example, the processor <NUM> controls all functions of the device <NUM> for determining quantization parameters in neural network by performing a program stored in the memory <NUM> on the device <NUM> for determining quantization parameters in neural network. The processor <NUM> may be implemented by a central processing unit (CPU), a graphics processing unit (GPU), an application processor (AP), an artificial intelligence processor chip (IPU), and the like provided by the device <NUM> for determining quantization parameters in neural network. However, the disclosure is not limited thereto.

The memory <NUM> is a hardware configured to store various data processed in the device <NUM> for determining quantization parameters in neural network. For example, the memory <NUM> may store processed data and data to be processed in the device <NUM> for determining quantization parameters in neural network. The memory <NUM> may further store a processed data set or a data set to be processed involved in the process of a neural network operation performed by the processor <NUM>, such as untrained initial neural network data, intermediate neural network data generated in the training process, neural network data which has completed all trainings, quantized neural network data, and the like. In addition, the memory <NUM> can store applications, drivers, and the like that are driven by the device <NUM> for determining quantization parameters in neural network. For example, the memory <NUM> can store various programs related to a training algorithm and a quantization algorithm of the neural network to be performed by the processor <NUM>. The memory <NUM> may be a DRAM, but the disclosure is not limited thereto. The memory <NUM> may include at least one of a volatile memory or a non-volatile memory. The non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a phase change random-access memory (PRAM), a magnetic RAM (MRAM), a resistive RAM (RRAM), a ferroelectric RAM (FeRAM), and the like. The volatile memory may include a dynamic RAM (DRAM), a static RAM (SRAM), a synchronous DRAM (SDRAM), PRAM, MRAM, RRAM, the ferroelectric RAM (FeRAM), and the like. In examples, the memory <NUM> may include at least one of a hard disk drive (HDD), a solid-state drive (SSD), a compact flash memory (CF), a secure digital (SD) card, a micro-secure digital (Micro-SD) card, a mini-secure digital (Mini-SD) card, an extreme digital (xD) card, a cache, or a memory stick.

The processor <NUM> may generate a trained neural network by repeatedly training (learning) a given initial neural network. In the state, parameters of the initial neural network may be in a high-precision data representation format, such as a data representation format with a precision of <NUM>-bit floating-point, while ensuring the processing precision of the neural network. The parameters may include various types of data input/output to/from the neural network, such as an input/output neuron, a weight, a bias, and the like. Compared with a fixed-point computation, a floating-point computation requires a relatively large number of computations and relatively frequent memory access. For example, most of the computations required for a neural network processing are known as various convolution computations. Therefore, in a mobile device with relatively low processing performance (such as a smart phone, a tablet, a wearable device, an embedded device, and the like. ), a neural network high-precision data computation may make resources of a mobile device underutilized. As a result, in order to drive the neural network computation within an allowable range of precision loss and reduce the amount of computation in the above-mentioned devices, the high-precision data involved in the neural network computation can be quantized and converted into low-precision fixed-point numbers.

Considering the processing performance of a device deployed with a neural network such as a mobile device and an embedded device, the device <NUM> for determining quantization parameters in neural network may convert parameters of a trained neural network into fixed-point quantization with a specific number of bits, and the device <NUM> for determining quantization parameters in neural network sends a corresponding quantization parameter to the device deployed with the neural network, so that the training, fine-tuning, and other operations performed by the artificial intelligence processor chip is a fixed-point computation. The device deployed with a neural network may be an autonomous vehicle, a robot, a smart phone, a tablet device, an augmented reality (AR) device, an Internet of Things (IoT) device, and the like which uses the neural network to perform voice recognition, image recognition, and the like, but the present disclosure is not limited thereto.

The processor <NUM> obtains data from the memory <NUM> in the process of neural network operation. The data includes at least one type of data among neurons, weights, biases, and gradients. A corresponding quantization parameter is determined by using the technical scheme shown in <FIG>, and target data in the process of neural network operation is quantized by using the quantization parameter to obtain quantized data. Then a neural network operation is performed on the quantized data, in which the operation includes but is not limited to training, fine-tuning, and inference.

The processor <NUM> adjusts the data bit width n according to the quantization error diffbit, and the processor <NUM> may determine the target iteration interval of the data bit width or the target iteration interval of the quantization parameter by executing the determination methods of a target iteration interval shown in <FIG>.

In summary, for the device for determining quantization parameters in neural network in examples of the specification, specific functions of the memory <NUM> and the processor <NUM> can be explained by referring to preceding examples in the specification, and may achieve the technical effects of the preceding examples, which will not be described further.

In an example, the processor <NUM> may be implemented in any appropriate manner. For example, the processor <NUM> may adopt a form such as a microprocessor, a processor, a computer-readable medium storing computer-readable program codes (such as software or firmware) which can be executed by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, an embedded microcontroller, and the like.

As shown in <FIG> is an application schematic diagram of a device for determining quantization parameters in neural network applied to an artificial intelligence processor chip according to an example of the present disclosure. Referring to <FIG>, as described above, in the device <NUM> for determining quantization parameters in neural network such as a PC and a server, the processor <NUM> performs a quantization operation and quantizes floating-point data involved in the neural network operation into fixed-point data, and the fixed-point data obtained by the quantization is used by a fixed-point computation unit on an artificial intelligence processor chip to perform training, fine-tuning, or inference. The artificial intelligence processor chip is a specific hardware configured to drive the neural network. Since the artificial intelligence processor chip is implemented with relatively low power or performance, low-precision fixed-point data is used to implement the neural network operation according to the technical scheme. Compared with high-precision data, a memory bandwidth required to read low-precision fixed-point data may be smaller, and the caches of the artificial intelligence processor chip may be better used to avoid a bottleneck of memory access. At the same time, when an SIMD instruction is executed on the artificial intelligence chip, more computations may be realized in one clock cycle, which may realize a faster neural network operation.

Furthermore, for a fixed-point computation and a high-precision data computation with a same length, a floating-point computation is more complex than the fixed-point computation and requires more logic components to compose a floating-point computation unit. Therefore, in terms of volume, the floating-point computation unit is larger than the fixed-point computation unit. Moreover, the floating-point computation unit requires more resources to process, and a power gap between the fixed-point computation unit and the floating-point computation unit is usually an order of magnitude.

In summary, the technical scheme may replace a floating-point computation unit with a fixed-point computation unit on an artificial intelligence processor chip, so as to make power consumption of an artificial intelligence processor chip lower, which is important for mobile devices. In other words, the technical scheme opens a door to a large number of embedded systems that may not run floating-point computing codes efficiently, which makes it possible for the Internet of Things to be widely used in the world.

In the technical scheme, an artificial intelligence processor chip may correspond to, for example, a neural processing unit (NPU), a tensor processing unit (TPU), a neural engine, and the like, which are specific chips for driving a neural network, but the present disclosure is not limited thereto.

In the technical scheme, an artificial intelligence processor chip may be implemented in a separate device independent of the device <NUM> for determining quantization parameters in neural network, and the device <NUM> for determining quantization parameters in neural network may also be implemented as a part of functional modules of the artificial intelligence processor chip, but the present disclosure is not limited thereto.

In the technical scheme, an operation system of a general-purpose processor (such as CPU) generates an instruction based on the present technical scheme, and then sends the generated instruction to an artificial intelligence processor chip (such as GPU). The artificial intelligence processor chip performs an instruction operation to determine a neural network quantization parameter and realize quantization. In another application, the general-purpose processor directly determines the corresponding quantization parameter based on the present technical scheme, and directly quantizes corresponding target data according to the quantization parameter. An artificial intelligence processor chip performs a fixed-point computation by using quantized data. Furthermore, the general purpose processor (such as CPU) and the artificial intelligence processor chip (such as GPU) perform a pipelining operation. The operating system of the general purpose processor (such as CPU) generates an instruction based on the present technical scheme, and copies the target data while the artificial intelligence processor chip (such as GPU) performs a neural network operation, which may hide some of the time spent. But the present disclosure is not limited thereto.

An example of the present disclosure also provides a readable storage medium, on which a computer program is stored, and when the computer program is executed, the quantization parameter determination method of the neural network is realized.

It can be seen that in the process of neural network operation, a quantization parameter is determined during quantization by using the technical scheme disclosed in the present disclosure. The quantization parameter is used by an artificial intelligence processor to quantize data involved in the process of neural network operation and convert high-precision data into low-precision fixed-point data, which may reduce storage space of the data involved in the process of neural network operation. For example, a conversion of float32 to fix8 may reduce a model parameter by four times. Smaller data storage space enables neural network deployment to occupy smaller space, thus on-chip memory of an artificial intelligence processor chip may store more data, which may reduce memory access data in the artificial intelligence processor chip and improve computing performance.

Those of ordinary skill in the art also know that besides implementing a client and a server in the form of pure computer readable program codes, the client and the server may also achieve the same functions in the form of a logic gate, a switch, a specific integrated circuit, a programmable logic controller, and an embedded microcontroller by means of performing logic programming on method steps. Therefore, the client and the server can be considered as a hardware component, and devices included in the client and the server which are used to realize various functions can be considered as a structure within the hardware component, or considered as either a software module used to implement a method or a structure within the hardware component.

As shown in <FIG> is a functional block diagram of a device for determining quantization parameters in neural network according to an example of the present disclosure, in which the device includes:.

The units or modules described as separate components may or may not be physically separated and the components illustrated as units or modules may or may not be physical units, which means that the units or the components may be in the same place or may be distributed to a plurality of network units. All or part of the units may be selected according to actual needs to achieve the purpose of the technical solutions of the examples.

In addition, unless otherwise specified, functional units/ modules in various examples of the present disclosure may be integrated into one unit/module, or each unit/module may be physically present, or two or more units/modules may be integrated into one unit/module. The above-mentioned integrated unit/module can be implemented in the form of hardware or a software program module.

It should be understood that the described device examples are merely illustrative and can be implemented in other manners; for instance, division of the unit/module is only a logical function division and can be divided in other manners during actual implementations, for example, a plurality of units, modules, or components may be combined or integrated into another system, or some features may be ignored, or not performed.

When the integrated unit/module is implemented in the form of hardware, the hardware may be a digital circuit, an analogue circuit, and the like. Physical implementation of a hardware structure includes, but is not limited to, a transistor, a memristor, and the like. Unless otherwise specified, the artificial intelligence processor may be any approriate hardware processor, for example, a CPU, a graphics processing unit (GPU), a field-programmable gate array (FPGA), a digital signal processor (DSP), and an application specific integrated circuit (ASIC). Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, for example, a resistive random-access memory (RRAM), a dynamic random-access memory (DRAM), a static random-access memory (SRAM), an enhanced dynamic random-access memory (EDRAM), a high-bandwidth memory (HBM), and a hybrid memory cube (HMC).

The integrated unit/module may be stored in a computer-readable memory when implemented in the form of a software program module and is sold or used as a separate product. Based on such understanding, the technical schemes of the present disclosure essentially, or the part of the technical schemes that contributes to the related art, or all or part of the technical solutions, may be embodied in the form of a software product which is stored in a memory and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device and so on) to perform all or part of the steps described in the various examples of the present disclosure. The memory includes various medium capable of storing program codes, such as a USB (universal serial bus) flash disk, a read-only memory (ROM), a random access memory (RAM), a removable hard disk, Disk, compact disc (CD), or the like.

In the present technical scheme, the present disclosure further discloses an artificial intelligence chip, which includes the device for determining quantization parameters in neural network.

In the present technical scheme, the disclosure further discloses a board card, which includes a memory device, an interface device, a controller device, and the artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the controller device, and the interface device respectively; the memory device is configured to store data; the interface device is configured to transmit data between the artificial intelligence chip and an external equipment; and the control device is configured to monitor a state of the artificial intelligence chip.

<FIG> is a structural block diagram of a board card according to an example of the present disclosure. Referring to <FIG>, the board card may include other support components besides a chip <NUM>, in which the other support components include but are not limited to: a memory device <NUM>, an interface device <NUM>, and a control device <NUM>;
the memory device <NUM> is connected with the artificial intelligence chip by a bus and is configured to store data. The memory device may include a plurality of storage units <NUM>. Each group of the storage unit is connected with the artificial intelligence chip by a bus. It is understandable that each group of the storage unit can be a double data rate SDRAM (DDR SDRAM).

The DDR may increase a speed of SDRAM by multiple times without increasing a clock frequency, and allow data to be read at a rising edge and a falling edge of a clock pulse. The speed of DDR is twice as fast as that of standard SDRAM. In an example, the storage device may include four groups of the storage units. Each group of the storage unit may include a plurality of DDR4 particles (chips). In an example, the artificial intelligence chip may include four <NUM>-bit DDR4 controllers. In the <NUM>-bit DDDR4 controllers, 64bit is used for data transmission and 8bit is used for ECC verification. It is understandable that a theoretical bandwidth of data transmission may reach 25600MB/s when DDR4-<NUM> particles are used in each group of the storage units.

In an example, each group of the storage units may include a plurality of DDR SDRAMs which are set in parallel. The DDR can transmit data twice in a clock cycle. A controller for controlling DDR is set in the chip for controlling data transmission and data storage of each storage unit.

The interface device is electrically connected with the artificial intelligence chip. The interface device is configured to realize data transmission between the artificial intelligence chip and an external device (such as a server or a computer). In an example, the interface device may be a standard PCIE interface. For example, data to be processed is transmitted to the chip by the server through the standard PCIE interface to realize data transmission. In another example, when a PCIE <NUM>. 0X <NUM> interface is used for transmission, the theoretical bandwidth may reach <NUM> MB/s. In another example, the interface device may be other interfaces, and the present disclosure is not intended to limit specific representations of the other interfaces, as long as the interface unit can realize transmission. In addition, a computation result of the artificial intelligence chip is still transmitted back to the external device (such as the server) by the interface device.

The control device is electrically connected with the artificial intelligence chip. The control device is configured to monitor the state of the artificial intelligence chip. For example, the artificial intelligence chip and the control device can be electrically connected through an SPI interface. The control device may include a microcontroller unit (MCU). The artificial intelligence chip may include multiple processing chips, multiple processing cores, or multiple processing circuits, which may drive multiple loads. Therefore, the artificial intelligence chip may work under different working states such as multi-load and light-load. The control device may be capable of regulating the working states of the multiple processing chips, the multiple processing chips, and the multiple processing circuits in the artificial intelligence chip.

Another example of the disclosure provides an electronic device, which includes the artificial intelligence chip. The electronic device includes a data processing device, a robot, a computer, a printer, a scanner, a tablet computer, an intelligent terminal, a mobile phone, a drive recorder, a navigator, a sensor, a webcam, a cloud server, a camera, a video camera, a projector, a watch, an earphone, a mobile storage, a wearable device, a transportation means, a household electrical appliance, and/or a medical device.

Claim 1:
A method of performing neural network training or neural network fine-tuning, which is implemented by an artificial intelligence processor, comprising:
determining quantization parameter(s), which comprises
obtaining an analyzing result of each type of the data to be quantized, wherein the data to be quantized includes at least one type of data among neurons, weights, gradients, and biases of the neural network; and
determining a corresponding quantization parameter according to the analyzing result of each type of data to be quantized and a data bit width,
using the quantization parameter to perform corresponding quantization on data which are to be used in a process of neural network operation in the neural network training or the neural network fine-tuning, whereby high-precision data are converted into low-precision fixed-point data,
using the quantized fixed-point data to perform the neural network operation in the neural network training or the neural network fine-tuning,
characterized in that the quantization parameter includes a point position parameter and a second scaling coefficient, and a following formula (<NUM>) is used to quantize the data to be quantized to obtain the quantized data Ix. <MAT> in the formula, s refers to the point position parameter, f<NUM> refers to the second scaling coefficient, where <NUM>s*f<NUM> is considered as a first scaling coefficient, and <MAT>; Z is the maximum absolute value of all numbers in the number field of the data to be quantized; Ix refers to a n-bit binary representation value of the data x after quantization; Fx refers to a floating-point value of the data x before quantization; and round refers to rounding calculation, alternatively, the round calculation in the formula (<NUM>) is replaced by ceiling calculation or flooring calculation.