Inventors
Steve Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, Dharmendra S Modha
Publication date
2023/11/21
Patent office
US
Patent number
11823054
Application number
16796397
Description
Learned step size quantization in artificial neural network is provided. In various embodiments, a system comprises an artificial neural network and a computing node. The artificial neural network comprises: a quantizer having a configurable step size, the quantizer adapted to receive a plurality of input values and quantize the plurality of input values according to the configurable step size to produce a plurality of quantized input values, at least one matrix multiplier configured to receive the plurality of quantized input values from the quantizer and to apply a plurality of weights to the quantized input values to determine a plurality of output values having a first precision, and a multiplier configured to scale the output values to a second precision. The computing node is operatively coupled to the artificial neural network and is configured to: provide training input data to the artificial neural network, and optimize the …
Scholar articles
S Esser, JL McKinstry, D Bablani, R Appuswamy… - US Patent 11,823,054, 2023