177 citations · 178 across the 2 of their papers we have counts for
7 papers
CircConv: A Structured Convolution with Low Complexity
Siyu Liao, Zhe Li, Liang Zhao +3
Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications. However, the lar…
Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing
Zhe Li, Ji Li, Ao Ren +5
Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications. Nevertheless, the deep structure has brought significant i…
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
Caiwen Ding, Ao Ren, Geng Yuan +5
Both industry and academia have extensively investigated hardware accelerations. In this work, to address the increasing demands in computational capability and memory requirement,…
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
Shuo Wang, Zhe Li, Caiwen Ding +4
Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately,…
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Yanzhi Wang, Zheng Zhan, Jiayu Li +6
Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of de…
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
Yanzhi Wang, Caiwen Ding, Zhe Li +8
Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and pe…