Qualcomm Incorporated

Matthias Reisser

Max Welling

Tijmen Pieter Frederik Blankevoort

Christos Louizos

Jilei Hou

Edward Harrison Teague

Xiaochun Zhu

Zhongze Wang

Ying Wang

Yadong Lu

Saurabh Kedar Pitre

A method for compressing a deep neural network includes determining a pruning ratio for a channel and a mixed-precision quantization bit-width based on an operational budget of a device implementing the deep neural network. The method further includes quantizing a weight parameter of the deep neural network and/or an activation parameter of the deep neural network based on the quantization bit-width. The method also includes pruning the channel of the deep neural network based on the pruning ratio.

Joint pruning and quantization scheme for deep neural networks

In one embodiment, a method of simulating an operation of an artificial neural network on a binary neural network processor includes receiving a binary input vector for a layer including a probabilistic binary weight matrix and performing vector-matrix multiplication of the input vector with the probabilistic binary weight matrix, wherein the multiplication results are modified by simulated binary-neural-processing hardware noise, to generate a binary output vector, where the simulation is performed in the forward pass of a training algorithm for a neural network model for the binary-neural-processing hardware.

Systems and methods for modifying neural networks for binary processing applications

A method for quantizing a neural network includes modeling noise of parameters of the neural network. The method also includes assigning grid values to each realization of the parameters according to a concrete distribution that depends on a local fixed-point quantization grid and the modeled noise and. The method further includes computing a fixed-point value representing parameters of a hard fixed-point quantized neural network.

Continuous relaxation of quantization for discretized deep neural networks

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Weesp, Netherlands

Matthias Reisser