PATENT CLAIM ANALYSIS

Application Number: 15934681
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["708", "501000"]

Abstract:
Disclosed herein are techniques for accelerating convolution operations or other matrix multiplications in applications such as neural network. A computer-implemented method includes receiving low-precision inputs for a convolution operation from a storage device, and subtracting a low-precision value representing a high-precision zero value from the low-precision inputs to generate difference values, where the low-precision inputs are asymmetrically quantized from high-precision inputs. The method also includes performing multiplication and summation operations on the difference values to generate a sum of products, and generating a high-precision output by scaling the sum of products with a scaling factor.

Claim (Index 1):
A computer-implemented method, comprising:\n receiving floating point inputs to a convolution layer of an artificial neural network; quantizing the floating point inputs linearly into n-bit integer inputs, wherein:\n n is a multiple of 8; \n a minimum value of the floating point inputs is quantized as integer zero; \n a maximum value of the floating point inputs is quantized as 2 n \u22121; and \n a zero value of the floating point inputs is quantized as an integer representing the zero value of the floating point inputs with no quantization error; \n storing the integer inputs in a memory; reading, by the convolution layer, the integer inputs from the memory; creating difference values by subtracting, by a subtraction engine, the integer representing the zero value of the floating point inputs from the integer inputs such that the zero value of the floating point inputs is represented by an integer value zero; performing, by the convolution layer, one set of (n+1)-bit integer multiplication and summation operations on the difference values to generate a sum of products; and generating, by an activation layer, a pooling layer, or an output buffer of the artificial neural network, a floating point output by scaling the sum of products with a floating point scaling factor.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 90.0
- Lexical Diversity: 1.65574
- Patent Class: 708.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15090696', '15090796', '14549350', '15487091', '15263772']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4142176372254592
- 35 USC 102 Novelty (BERT): 0.4624420737186617
- Combined Prediction Score: 0.4190400808747795
- Mean Citation Score: 164.112174
- Max Citation Score: 166.71759
- Similarity Product: 139.07326383437098

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test