8 citations · 17 across the 3 of their papers we have counts for
6 papers
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
Yang Zhao, Xiaohan Chen, Yue Wang +6
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep ne…
TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain
Weitao Li, Pengfei Xu, Yang Zhao +3
Resistive-random-access-memory (ReRAM) based processing-in-memory (RPIM) accelerators show promise in bridging the gap between Internet of Thing devices' constrained resources…
A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision
Hongjie Wang, Yang Zhao, Chaojian Li +2
The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various e…
DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures
Yang Zhao, Chaojian Li, Yue Wang +3
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators. However, designing DNN accelerators is non-trivial as it…
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs
Pengfei Xu, Xiaofan Zhang, Cong Hao +7
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growing demand for DNN chips. However, designing DNN chips is non-trivial because: (1) mainstream DNNs have millio…
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
Yue Wang, Ziyu Jiang, Xiaohan Chen +4
Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained pla…