1 citations · 1 across the 5 of their papers we have counts for
5 papers
Efficient yet Accurate End-to-End SC Accelerator Design
Meng Li, Yixuan Hu, Tengyu Zhang +4
Providing end-to-end stochastic computing (SC) neural network acceleration for state-of-the-art (SOTA) models has become an increasingly challenging task, requiring the pursuit of…
DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging
Qinggang Zhou, Yawen Zhang, Pengcheng Li +4
The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets. Minibatch stochastic gradient de…
An Energy-Efficient Mixed-Signal Parallel Multiply-Accumulate (MAC) Engine Based on Stochastic Computing
Xinyue Zhang, Jiahao Song, Yuan Wang +4
Convolutional neural networks (CNN) have achieved excellent performance on various tasks, but deploying CNN to edge is constrained by the high energy consumption of convolution ope…
Memory System Designed for Multiply-Accumulate (MAC) Engine Based on Stochastic Computing
Xinyue Zhang, Yuan Wang, Yawen Zhang +5
Convolutional neural network (CNN) achieves excellent performance on fascinating tasks such as image recognition and natural language processing at the cost of high power consumpti…
A Parallel Bitstream Generator for Stochastic Computing
Yawen Zhang, Runsheng Wang, Xinyue Zhang +5
Stochastic computing (SC) presents high error tolerance and low hardware cost, and has great potential in applications such as neural networks and image processing. However, the bi…