Systems and methods for determining an artificial intelligence model in a communication system

A system may include multiple client devices and a processing device communicatively coupled to the client devices. Each client device includes an artificial intelligence (AI) chip and is configured to generate an AI model. The processing device may be configured to (i) receive a respective AI model and an associated performance value of the respective AI model from each of the plurality of client devices; (ii) determine an optimal AI model based on the performance values associated with the respective AI models from the plurality of client devices; and (iii) determine a global AI model based on the optimal AI model. The system may load the global AI model into an AI chip of a client device to cause the client device to perform an AI task based on the global AI model in the AI chip. The AI model may include a convolutional neural network.

FIELD

This patent document relates generally to systems and methods for providing artificial intelligence solutions. Examples of determining an artificial intelligence model for loading into an artificial intelligence chip in a communication system are provided.

BACKGROUND

Artificial intelligence solutions are emerging with the advancement of computing platforms and integrated circuit solutions. For example, an artificial intelligence (AI) integrated circuit (IC) may include a processor capable of performing AT tasks in embedded hardware, Hardware-based solutions, as well as software solutions, still encounter the challenges of obtaining an optimal AI model, such as a convolutional neural network (CNN). A CNN may include multiple convolutional layers, and a convolutional layer may include multiple weights. Given the increasing size of the CNN that can be embedded in an IC, a CNN may include hundreds of layers and may include tens of thousands of weights. For example, the size of the weights for an embedded CNN inside an AI chip may be as large as a few megabytes. This makes it difficult to obtain an optimal CNN model because a large amount of computing times are needed.

DETAILED DESCRIPTION

As used in this document, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. As used in this document, the term “comprising” means “including, but not limited to.” Unless defined otherwise, all technical and scientific terms used in this document have the same meanings as commonly understood by one of ordinary skill in the art.

Each of the terms “artificial intelligence logic circuit” and “AI logic circuit” refers to a logic circuit that is configured to execute certain AI functions such as a neural network in AI or machine learning tasks. An AI logic circuit can be a processor. An AI logic circuit can also be a logic circuit that is controlled by an external processor and executes certain AI functions.

Each of the terms “integrated circuit,” “semiconductor chip,” “chip,” and “semiconductor device” refers to an integrated circuit (IC) that contains electronic circuits on semiconductor materials, such as silicon, for performing certain functions. For example, an integrated circuit can be a microprocessor, a memory, a programmable array logic (PAL) device, an application-specific integrated circuit (ASIC), or others. An integrated circuit that contains an AI logic circuit is referred to as an AI integrated circuit.

The term “AI chip” refers to a hardware- or software-based device that is capable of performing functions of an NI logic circuit. An AI chip can be a physical IC. For example, a physical AI chip may include an embedded cellular neural network (CeNN), which may contain parameters of a CNN. The AI chip may also be a virtual chip, i.e., software-based. For example, a virtual AI chip may include one or more process simulators to simulate the operations of a physical AI IC.

The term of “AI model” refers to data that include one or more weights that are used for, when loaded inside an AI chip, executing the AI chip. For example, an AI model for a given CNN may include the weights and/or parameters for one or more convolutional layers of the CNN.

FIG. 1illustrates an example system in accordance with various examples described herein. In some examples, a communication system100includes a communication network102. Communication network102may include any suitable communication links, such as wired (e.g., serial, parallel, optical, or Ethernet connections) or wireless (e.g., Wi-Fi, Bluetooth, or mesh network connections), or any suitable communication protocols now or later developed. In some scenarios, system100may include one or more host devices, e.g.,110,112,114,116. A host device may communicate with another host device or other devices on the network102. A host device may also communicate with one or more client devices via the communication network102. For example, host device110may communicate with client devices120a,120b,120c,102d, etc. Host device112may communicate with130a,130b,130c,130d, etc. Host device114may communicate with140a,140b,140c, etc. A host device, or any client device that communicates with the host device, may have access to one or more datasets used for obtaining an AI model. For example, host device110or a client device such as120a,120b,120c, or120dmay have access to dataset150.

InFIG. 1, a client device may include a processing device. A client device may also include one or more AI chips. In some examples, a client device may be an AI chip. The AI chip may be a physical AI IC. The AI chip may also be software-based, i.e., a virtual AI chip that includes one or more process simulators to simulate the operations of a physical AI IC. A processing device may include an AI IC and contain programming instructions that will cause the AI IC to be executed in the processing device. Alternatively, and/or additionally, a processing device may also include a virtual AI chip, and the processing device may contain programming instructions configured to control the virtual AI chip so that the virtual AI chip may perform certain AI functions. InFIG. 1, each client device, e.g.,120a,120b,120c,120dmay be in electrical communication with other client devices on the same host device, e.g.,110, or client devices on other host devices.

In some examples, the communication system100may be a centralized system. System100may also be a distributed or decentralized system, such as a peer-to-peer (P2P) system, example, a host device, e.g.,110,112,114, and116, may be a node in a P2P system. In a non-limiting example, a client devices, e.g.,120a,120b,120c, and120dmay include a processor and an AI physical chip. In another non-limiting example, multiple AI chips may be installed in a host device. For example, host device116may have multiple AI chips installed on one or more PCI boards in the host device or in a USB cradle that may communicate with the host device. Host device116may have access to dataset156and may communicate with one or more AI chips via PCI board(s), internal data buses, or other communication protocols such as universal serial bus (USB).

In some scenarios, the AI chip may contain an AI model for performing certain AI tasks. In some examples, an AI model may include a forward propagation neural network, in which information may flow from the input layer to one or more hidden layers of the network to the output layer. For example, an AI model may be a convolutional neural network (CNN) that is trained to perform voice or image recognition tasks. A CNN may include multiple convolutional layers, each of which may include multiple weights and parameters. In such case, an AI model may include one or more parameters and/or weights of the CNN model. In some examples, the weights of a CNN model may include a mask and a scalar for a given layer of the CNN model. For example, a kernel in a CNN layer may be represented by a mask that has multiple values in lower precision multiplied by a scalar in higher precision. In some examples, an output channel of a CNN layer may include one or more bias values that, when added to the output of the output channel, adjust the output values to a desired range.

In the case of physical AI chip, the AI chip may include an embedded cellular neural network that has a memory for containing the multiple weights in the CNN. In some scenarios, the memory in a physical AI chip may be a one-time-programmable (OTP) memory that allows a user to load a CNN model into the physical AI chip once. Alternatively, a physical AI chip may have a random access memory (RAM) or other types of memory that allows a user to update and load a CNN model into the physical AI chip multiple times.

In the case of virtual AI chip, the AI chip may include a data structure to simulate the cellular neural network in a physical AI chip. A virtual AI chip can be of particular advantageous when multiple tests need to be run over various CNNs in order to determine a model that produces the best performance (e.g., highest recognition rate or lowest error rate). In a test run, the weights in the CNN can easily vary and be loaded into the virtual AI chip without the cost associated with a physical AI chip. Only after the CNN model is determined will the CNN model be loaded into a physical AI chip for real-time applications. Training a CNN model may require significant amount of computing power, even with a physical AI chip because a CNN model may include tens of thousands of weights. For example, a modern physical AI chip may be capable of storing a few megabytes of weights inside the chip.

With further reference toFIG. 1, a host device on a communication network as shown inFIG. 1(e.g.,110) may include a processing device and contain programming instructions that, when executed, will cause the processing device to access a dataset, e.g.,150, for example, test data. The test data may be provided for use in obtaining the AI model. In doing so, the AI model may be specific to the test data. For example, test data may be used for training an AI model that is suitable for face recognition tasks, and may contain any suitable dataset collected for performing face recognition tasks. In another example, test data may be used for training an AI model suitable for scene recognition in video and images, and may contain any suitable scene dataset collected for performing scene recognition tasks. In some scenarios, test data may be residing in a memory in a host device. In one or more other scenarios, test data may be residing in a central data repository and is available for access by any of the host devices (e.g.,110,112,114inFIG. 1) or any of the client devices (e.g.,120a-d,140a-dinFIG. 1) via the communication network102. In some examples, system100may include multiple test sets, such as datasets150,152,154. A CNN model may be obtained by using the multiple devices in a communication system such as shown inFIG. 1. Details are further described with reference toFIGS. 2-3.

FIG. 2illustrates a diagram of an example process for obtaining a global optimal AI model in accordance with various examples described herein. In some examples, a host device (such as110inFIG. 1) may be configured to program one or more client devices or one or more AI chips to which the host device is communicating (120a,120b,120c,120dunder host device110, or one or more AI chips under host116) to cause the multiple client devices or AI chips to determine an AI model for that host device. For example, a process200, which may be implemented in a host device (e.g.,110,112,114inFIG. 1), may include providing initial AI models at202for the client devices under the host device. Process200may also include transmitting the initial AI models at204to the client devices and/or AI chips. In some examples, the initial AI models may include multiple initial AI models, each for a respective client device or an AI chip (under the host device). The initial AI models may be identical for all or some client devices or AI chips. Initial AI models may also be different among different client devices or AI chips. Once a client device or an AI chip receives a respective initial AI model, that client device or AI chip may execute an AI task using the initial AI model to generate a respective updated AI model, which process may further be described.

With further reference toFIG. 2, process200may include receiving updated AI models at206from the one or more client devices (or AI chips). In some examples, a client device may return a client device updated AI model to the host device. The host device subsequently receives multiple AI models, each from a client device. Process200may subsequently determine the optimal AI model for the host device at207based on the updated AI models of one or more client devices and a performance value associated with each AI model. Process200may repeat for a number iterations until the iteration count has exceeded a threshold Tcat214or the time duration of the process has exceeded a threshold TDat216. At each iteration, process200continues receiving updated AI models from the client devices at206and determining the optimal AI model for the host device at207. For example, M″i,0, M″i,1, . . . , M″i,N-1represent the updated AI model from each client device 0, 1, 2, . . . N−1, respectively, at ith iteration, where N represents the number of client devices under the host device. Let A″i,0, A″i,1, . . . , A″i,N-1stand for the performance value of the updated AI model from each client device at ith iteration.

In some examples, a model M may include one or more parameters and/or weights of the CNN model, such as the scalar, the mask, and the bias values. Model M may have any suitable data structure. For example, model M may include a flat one-dimensional (1D) structure that holds the CNN parameters and weights sequentially from a few bytes to a few megabytes or more. The parameters may depend on the CNN model, the M task for which the AI model is to be obtained, and the dataset for performing the AI task using the AI chip. For example, an AI task having different levels of difficulties may require different sets of CNN parameters.

In some examples, a performance value A may include a single value measured as the recognition accuracy associated with an AI model M, such as the updated AI model from a client device. For example, A″i,0may stand for the performance of model M″i,0and have a value of 0.5. If Hi,jstands for the optimal AI model for the host device j at ith iteration, where j=0, 1, . . . , K−1, with K being the number of hosts in the network, then Hi,jmay be determined as Hi,j=E(M″i,0, M″i,1, . . . , M″i,N-1, A″i,0, A″i,1, . . . , A″i,N-1In other words, at each iteration, the optimal AI model for a host may be determined based on the received updated AI models and associated performance values from one or more client devices under that host. In a non-limiting example, a host device may determine the optimal AI model for that host device by selecting a received updated AI model that has the best performance value among all client devices under that host. For example, if the performance value represents the accuracy of recognition using an AI model, then selecting the best performance includes selecting an AI model that has the highest performance value among all client devices under the host device.

Although it is illustrated that, at each iteration, the optimal AI model for a host may be determined based on the received AI models and associated performance values from one or more client devices under that host, other variations may be possible. For example, the optimal AT model may be determined based on criteria other than the best performance value. In some examples, the optimal AI model for a host device may be determined based on the performance value of a subset of the client devices under that host device. For example, the process may select among top five of a total of ten client devices, or remove the bottom two client devices, in terms of performance value of the AI model for each client device.

Returning toFIG. 2, process200may further determine a global AI model at209based on the received AI models from the client devices. At each repeat (iteration), process200continues to update the global AI model at209and increments the iteration count at212. If the iteration count has exceeded the threshold Tcat214or the time duration has exceeded the threshold TDat216, the process ends at218. In some scenarios, when the process ends, the global optimal AI model is obtained as the final global AI model in process200. In some examples, the process may output the final global AI model, as the global optimal AI model, to the one or more hosts on the network. Upon receiving the final global AI model, a host device may load the global optimal AI model into one or more client devices under that host device such as the AI chip in the client devices for performing future AI recognition tasks. In some examples, the global optimal AI model may be shared among multiple processing devices on the network, in which any device may load the global optimal AI model into an embedded CeNN and execute CeNN to perform recognition tasks based on the global optimal AI model. If none of the thresholds have been reached, process200repeats transmitting the updated initial AI models to the client devices at204. The global AI model after the iteration has ended will be the final global AI model. At this time, process200has obtained the final AI model for the system.

In determining the global AI: model at209at each iteration, the process may select the optimal AI model that has the best performance value among all host devices. For example, a host device may determine the optimal AI model for that host device at207and make that optimal AI model sharable among other host devices on the network. In a non-limiting example, process200may include accessing all other host devices and receiving information about their optimal AI models at208. Let Hi,0, Hi,1, . . . , Hi,K-1stand for the optimal AI model for host j=0, 1, . . . , K−1, where K is the number of host devices in an outer iteration. Process200may determine that global AI model H′i,j=U(Hi,0, Hi,1, . . . , Hi,K-1). In a non-limiting example, function U may include selecting the model with the best performance value. For example, in an outer iteration, a host device may access one or more other host devices and access information about the optimal AI model and associated performance value of those other host devices, and determine the global optimal AI model based on the optimal AI model for the host device itself and the optimal AI models of other host devices. Alternatively and/or additionally, a host device may determine the global optimal AI model based on an average of the optimal AI models among multiple host devices on the network.

In some examples, an AI model may include a one dimensional column vector, which contains all of the parameters of the AI model arranged sequentially in 1D. A subtraction of two AI models may include multiple parameters, each of which is a subtraction of two corresponding parameters in the two AI models. An addition of two AI models may include multiple parameters, each of which is a sum of two corresponding parameters in the two AI models. An average of multiple AI models may include parameters, each of which is an average of the corresponding parameters in the multiple AI models. Similarly, an AI model may be incremented (increased or decreased) by a perturbation, and the resulting model may contain multiple parameters, each of which includes a corresponding parameter in the AI model incremented by a corresponding parameter in the perturbation. In some examples, an addition of two AI models may be in discrete or finite field. For example, the addition of scalars and biases in two (or multiple) CNN models may be done in a real coordinate space. In another example, the addition of masks in multiple CNN models may be done in finite field.

At each iteration, process200may continue receiving information about other host devices at208and updating the global AI model at209based on the performance values of optimal AI models among multiple host devices. In some examples, process200may determine the global AI model at209based on the optimal AI models of all of the host devices on the network. In some examples, process200may determine the global AI model at209based on the optimal AI models of a subset of host devices on the network. For example, the process may only analyze top five optimal AI models from five host devices. Alternatively and/or additionally, the process may remove bottom two host devices in terms of performance values and analyze the optimal AI models of the remaining host devices.

With further reference toFIG. 2, at each iteration, process200may further include generating updated initial AI models at210. This updates the initial AI models for the client device(s) under the host device, thus the training process in each client device may “restart.” In other words, process200may find the global AI model at each iteration (e.g.,209) and cause a training process at a client device to update the initial AI model for the client device. For example, at dth iteration, and for client device i, where i=0, 1, . . . N−1 (N is the number of client devices under the host device), the host device may maintain the current initial AI model at previous iteration Mi_d-1, an updated AI model Mi_op(referred to as the local optimal AI model of the client device)), and the global AI model Mglobalacross all host devices. For example, the current AI model Mi_d-1and updated AI model Mi_opmay be obtained from box206for a corresponding client device, the global AI model Mglobalmay be obtained from box209. Process200may optimize the training process by adjusting the velocity of AI model.

In some examples, the process may determine a velocity of AI model ΔMi_dat the current iteration d based on the velocity of AI model at its previous iteration ΔMi_(d-1). The new velocity ΔMi_dmay also be determined based on the closeness of the current initial AI model for the client device relative to the local optimal AI model for that client device. The new velocity of AI model may also be based on the closeness of the current AI model relative to the global AI model. The closer the current AI model is to the local optimal AI model and/or the global AI model, the lower the velocity of AI model for the next iteration may be. For example, a velocity for client device i at the current dth iteration may be expressed as:
ΔMi_d−w*ΔMi_(d-1)+c1*r1*(Mi_op−Mi_d-1)−c1*r1*(Mglobal−Mi_d-1)
where w is the inertial coefficient, c1 and c2 are acceleration coefficients, r1 and r2 are random numbers. In some examples, w may be a constant number selected between [0.8, 1.2], c1 and c2 may be constant numbers in the range of [0, 2]. Random numbers r1 and r2 may be generated at each iteration d. The determination of velocity of AI model described herein may allow the training process to have a new model at each iteration moving towards the local optimal AI model (per client device) and the global optimal model of the system.

In some examples, an AI model, such as Mi_d-1, may be a column vector, e.g., an n×1 matrix, containing all of the parameters of the AI model arranged sequentially in 1D. A subtraction of two AI models, such as Mglobal−Mi_d-1may also be a column vector containing multiple parameters, each of which is a subtraction of two corresponding parameters in Mglobaland Mi_d-1. In some examples, r1 and r2 may be diagonal matrices, for example, n×n matrices, for which each parameter in the column vector corresponds to different randomly-generated r1 and r2. As such, the training process, such as process200, becomes an n-dimensional optimization problem. As described herein, the velocity of an AI model, e.g., ΔMi_d, ΔMi_(d-1), may contain the same number of parameters as that in the AI model and have the same dimension as the AI model. Once the velocity ΔMi_dis determined, the process may increment the current initial AI model at the previous iteration by the new velocity to determine an updated initial AI model. For example, the updated initial AI model for device i may be determined as Mi_d=Mi_d-1+ΔMi_d. Process200may determine the updated initial models for all of the client devices under the host device in a similar manner. Upon completion of the process at218, process200may further transmit the updated initial AI models to a respective client device.

NowFIG. 3illustrates a diagram of an example process for obtaining a local AI model that may be implemented in a processing device, such as a client device. A process300, which may be implemented in a client device, a host device and/or an AI chip, such as shown inFIG. 1, may train an AI model via one or more iterations. In each iteration, process300may receive the initial AI model for the client device at304. For example, at the beginning of the training process, an initial AI model may be defined for some or all of the client devices, and process300may receive the initial AI model. Once the training process (e.g.,200inFIG. 2) has started iterations, process300may receive an updated initial AI model, which may be determined by a host device of the client device (e.g.,210inFIG. 2). Process300may also receive one or more test datasets at302. For example, the dataset may be residing on any of the devices (host or client devices) on the communication network (e.g.,102inFIG. 1) and may be accessible to any other devices.

Process300may also determine an updated AI model at306based on the received initial AI model. In some examples, the process may generate an updated model by incurring a perturbation to the initial AI model. For example, at mth iteration in process300, an updated AI model for client device i may be represented as Mi_m=Mi_m-1+ΔM, where ΔM is the perturbation. In some examples, process300may include a simulated annealing process in which a small change to the parameters of the AI model are made. For example, an AI model may include three groups of parameters: the scalar, the mask (kernels), and the bias. In a CNN model, a computation in a given layer in the CNN may be expressed by Y=w*X+b, where X is input data, Y is output data in the given layer, w is a kernel, and b is a bias. Operation “*” is a convolution. Kernel w may include binary values. For example, a kernel may include 9 cells in a 3×3 mask, where each cell may have a binary value, such as “1” and “−1.” In such case, a kernel may be expressed by multiple binary values in the 3×3 mask multiplied by a scalar. The scalar may include a value having a bit width, such as 12-bit or 16-bit. Other bit length may also be possible. By multiplying each binary value in the 3×3 mask with the scalar, a kernel may contain values of higher bit-length. Alternatively, and/or additionally, a kernel may contain data with n-value, such as 7-value. The bias b may contain a value having multiple bits, such as 12 bits. Other bit length may also be possible.

Returning to block306inFIG. 3, updating the AI model may including updating one or more parameters of the AI model with a probability to change and an amplitude of change for a group of parameters. For example, the probabilities to change the scalar, the mask and the bias may each be 0.01, 0.001, and 0.01, respectively. The amplitude of change for scalar and bias may be 0.001. In an example implementation, the process may generate a random number, e.g., in the range of 0 and 1.0, and compare the random number to the probabilities for the group of parameters. If the random number exceeds the probability for a given group of parameters, that group of parameters may change according to the amplitude of change. In case of the previous example, a random number may be generated. If the random number is greater than 0.01, the process may subsequently change the scalar by 0.001. In changing the values in a mask, the process may change each value in the mask to its neighboring value. For example, if a value in a mask is a binary having two values {+1, −1}, each change of value may become a switching between the two values (−1 or +1).

With further reference toFIG. 3, process300may further including inferring the performance of the updated AI model by running the AI chip in the client device to generate a recognition result based on the updated AI model at308and determining the performance value of the updated AI model at310. In some examples, running the AI chip in the client device may include causing a processing device in the client device to execute a recognition task in the AI chip where an embedded CeNN of the AI chip contains the updated AI model, such as a CNN. In other words, if the AI chip is a hardware-based chip, the parameters of the updated AI model are loaded into the CeNN of the AI chip for performing the recognition tasks. A recognition task may depend on the dataset. For example, a dataset may include sample training images of scenes for a scene recognition task. For a recognition task using the dataset, a performance value may be measured against the AI model being used. For example, an accuracy value may be determined at310based on the result of a given recognition task using the updated AI model.

In some examples, process300maintains the current AI model and associated performance value at each iteration. A client device may also receive from its host device or have access to the optimal AI model of the host device among all client devices on the host and/or the associated performance value of the optimal AI model. An example of obtaining an optimal AI model of a host device is shown in207inFIG. 2. Upon determining the performance value of the updated AI model, process300may further determine whether to replace the current AI model with the updated AI model so that the process is able to maintain the optimal AI model at any time. In some examples, process300may determine to replace the current AI model with the updated AI model with a probability, which indicates a probability that the current AI model in the client device be replaced by the updated AI model. This probability may be determined based on the performance value of the updated AI model relative to the past performance value in the previous iteration. For example, a probability (for replacing the current AI model) may have a value of one (100%) if the updated AI model has a performance value that is better than the performance value of the optimal AI model of the host on which the current client device is residing.

Alternatively, and/or additionally, if the updated AI model has a performance value that is no better than the performance value of the optimal AI mode of the host, process300may still have a probability to replace the current AI model with the updated AI model. This may prevent the process from being “locked” into a local optimal point permanently so that the process can get on a healthy convergence curve to achieve a global optimal AI model. In an example implementation, the process may generate a random number, e.g., in the range of 0 and 1.0, and compare the random number to the probabilities for replacing the current AI model. If the random number exceeds the probability, that process may determine that the current AI model be replaced by the updated AI model. Otherwise, the process may continue without replacing the current AI model with the updated AI model.

In a non-limiting example, the probability for replacing the current AI model may decrease as the performance value of the updated AI model gets closer to the optimal AI model of the host device this is because, once the performance value of the AI model in the training is approaching an optimal value, the process may tend to converge and the probability of replacing the optimal AI model may diminish. Similarly, if the training process is on a healthy curve, it means that the training process should converge as time passes by. As such, the probability of replacing the optimal AI model should decrease as the number of iterations increases. In a non-limiting example, the probability may be determined as:
p=e−(Aop-Am)*m/C
where Aopis the performance value of the optimal AI model of the host that hosts the client device, Amis the current AI model in the client device, m is the number of iterations, and C is a constant factor. For example, C may be selected as 0.001. Other variations of determining the probability may also be possible.

With further reference toFIG. 3, if it is determined that the current AI model be replaced by the updated AI model, process300may proceed with replacing the current AI model with the updated AI model at314and repeats the iteration at304. If it is determined that the current AI model not be replaced by the updated AI model, the process may repeat the iteration at304, provided that the number of iterations has not exceeded a threshold T at316. If the number of iterations has exceeded the threshold T, the process may stop the iteration and transmit the current AI model to the host device at318. At this point, the current AI model may be noted as a local optimal AI model of the client device. In a host device, a training process (e.g., process200inFIG. 2) may receive the updated AI models (or local optimal AI models) from the client devices under that host device (e.g.,206inFIG. 2) and continue executing one or more steps in that training process to obtain the global AI model.

It is appreciated that the disclosures of various embodiments inFIGS. 1-4may vary. For example, the number of iterations in process200inFIG. 2and the number of iterations in process300inFIG. 3may be independent. For example, the number of iterations for a client device may be in the range of 10-100, and the number of iterations for a host device may be 100. Other values may also be possible.

FIG. 4depicts an example of internal hardware that may be included any electronic device or computing system for implementing various methods in the embodiments described inFIGS. 1-3. An electrical bus400serves as an information highway interconnecting the other illustrated components of the hardware. Processor405is a central processing device of the system, configured to perform calculations and logic operations required to execute programming instructions. As used in this document and in the claims, the terms “processor” and “processing device” may refer to a single processor or any number of processors in a set of processors that collectively perform a process, whether a central processing unit (CPU) or a graphics processing unit (GPU) or a combination of the two. Read only memory (ROM), random access memory (RAM), flash memory, hard drives, and other devices capable of storing electronic data constitute examples of memory devices425. A memory device, also referred to as a computer-readable medium, may include a single device or a collection of devices across which data and/or instructions are stored.

The hardware may also include a user interface sensor445that allows for receipt of data from input devices450such as a keyboard, a mouse, a joystick, a touchscreen, a remote control, a pointing device, a video input device, and/or an audio input device, such as a microphone. Digital image frames may also be received from an imaging capturing device455such as a video or camera that can either be built-in or external to the system. Other environmental sensors460, such as a GPS system and/or a temperature sensor, may be installed on system and communicatively accessible by the processor405, either directly or via the communication ports440. The communication ports440may also communicate with the AI chip to upload or retrieve data to/from the chip. For example, the global optimal AI model may be shared by all of the processing devices on the network. Any device on the network may receive the global AI model from the network and upload the global AI model, e.g., CNN weights, to the AI chip via the communication port440and an SDK (software development kit). The communication port440may also communicate with any other interface circuit or device that is designed for communicating with an integrated circuit.

Optionally, the hardware may not need to include a memory, but instead programming instructions are run on one or more virtual machines or one or more containers on a cloud. For example, the various methods illustrated above may be implemented by a server on a cloud that includes multiple virtual machines, each virtual machine having an operating system, a virtual disk, virtual network and applications, and the programming instructions for implementing various functions in the robotic system may be stored on one or more of those virtual machines on the cloud.

Various embodiments described above may be implemented and adapted to various applications. For example, the AI chip having a CeNN architecture may be residing in an electronic mobile device. The electronic mobile device may use the built-in AI chip to produce recognition results and generate performance values. In some scenarios, obtaining the CNN can be done in the mobile device itself, where the mobile device retrieves test data from a dataset and uses the built-in AI chip to perform the training. In other scenarios, the processing device may be a server device in the communication network (e.g.,102inFIG. 1) or may be on the cloud. These are only examples of applications in which an AI task can be performed in the AI chip.

The various systems and methods disclosed in this patent document provide advantages over the prior art, whether implemented standalone or combined. For example, using the systems and methods described inFIGS. 1-4may help obtain the global optimal AI model using multiple networked devices in either centralized or decentralized or distributed network. This networked approach helps the system to narrow the search space of the AI model during the training process thus the system may converge to the global optimal AI model faster. The above disclosed embodiments also allow different training methods to be adapted to obtain the global optimal AI model, whether test data dependent or test data independent. For example, a client device may implement its own training process to obtain the local optimal AI model. Above illustrated embodiments are described in the context of generating a CNN model for an AI chip (physical or virtual), but can also be applied to various other applications. For example, the current solution is not limited to implementing the CNN but can also be applied to other algorithms or architectures inside an AI chip.

It will be readily understood that the components of the present solution as generally described herein and illustrated in the appended figures could be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of various implementations, as represented in the figures, is not intended to limit the scope of the present disclosure, but is merely representative of various implementations. While the various aspects of the present solution are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The present solution may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the present solution is, therefore, indicated by the appended claims rather than by this detailed description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Other advantages can be apparent to those skilled in the art from the foregoing specification. Accordingly, it will be recognized by those skilled in the art that changes, modifications, or combinations may be made to the above-described embodiments without departing from the broad inventive concepts of the invention. It should therefore be understood that the present solution is not limited to the particular embodiments described herein, but is intended to include all changes, modifications, and all combinations of various embodiments that are within the scope and spirit of the invention as defined in the claims.