International Business Machines Corporation

Jeffrey L McKinstry

Dharmendra S Modha

Rathinakumar Appuswamy

Steve Kyle Esser

John Vernon Arthur

Myron Dale Flickner

Andrew S Cassidy

Jun Sawada

Pallab Datta

Brian Taba

Deepika Bablani

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 configurable step size based on a gradient through the quantizer and the training input data.

Learned step size quantization

Neural inference chips are provided. A neural core of the neural inference chip comprises a vector-matrix multiplier; a vector processor; and an activation unit operatively coupled to the vector processor. The vector-matrix multiplier, vector processor, and/or activation unit is adapted to operate at variable precision.

Flexible precision neural inference processing unit

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San Jose, CA, United States of America

Jeffrey L McKinstry