90 citations · 309 across the 11 of their papers we have counts for
11 papers
SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training
Xiaohan Chen, Yang Zhao, Yue Wang +6
The record-breaking performance of deep neural networks (DNNs) comes with heavy parameterization, leading to external dynamic random-access memory (DRAM) for storage. The prohibiti…
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…
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…
Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference
Jianghao Shen, Yonggan Fu, Yue Wang +3
While increasingly deep networks are still in general desired for achieving state-of-the-art performance, for many specific inputs a simpler network might already suffice. Existing…
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…
Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks
Haoran You, Chaojian Li, Pengfei Xu +6
(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comp…
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
Yue Wang, Jianghao Shen, Ting-Kuei Hu +5
State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widel…
Performance Evaluation of Deep Learning Tools in Docker Containers
Pengfei Xu, Shaohuai Shi, Xiaowen Chu
With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to…
Supervised Learning Based Algorithm Selection for Deep Neural Networks
Shaohuai Shi, Pengfei Xu, Xiaowen Chu
Many recent deep learning platforms rely on third-party libraries (such as cuBLAS) to utilize the computing power of modern hardware accelerators (such as GPUs). However, we observ…
Benchmarking State-of-the-Art Deep Learning Software Tools
Shaohuai Shi, Qiang Wang, Pengfei Xu +1
Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Traini…