177 citations · 689 across the 74 of their papers we have counts for
9 papers · 1 filter
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Zhe Li, Caiwen Ding, Siyue Wang +8
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types…
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…
Learning Topics using Semantic Locality
Ziyi Zhao, Krittaphat Pugdeethosapol, Sheng Lin +4
The topic modeling discovers the latent topic probability of the given text documents. To generate the more meaningful topic that better represents the given document, we proposed…
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,…
Prediction-Based Fast Thermoelectric Generator Reconfiguration for Energy Harvesting from Vehicle Radiators
Hanchen Yang, Feiyang Kang, Caiwen Ding +7
Thermoelectric generation (TEG) has increasingly drawn attention for being environmentally friendly. A few researches have focused on improving TEG efficiency at the system level o…
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Zhe Li, Shuo Wang, Caiwen Ding +3
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning…
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,…
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…
VIBNN: Hardware Acceleration of Bayesian Neural Networks
Ruizhe Cai, Ao Ren, Ning Liu +5
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probabi…