2 papers
cs.LG2018
Tetris: Re-architecting Convolutional Neural Network Computation for Machine Learning Accelerators
Hang Lu, Xin Wei, Ning Lin +2
Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffect…
cs.LG2018
AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference
Xin He, Liu Ke, Wenyan Lu +2
The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate co…