168 citations · 743 across the 44 of their papers we have counts for
10 papers · 1 filter
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
Yichi Zhang, Junhao Pan, Xinheng Liu +3
Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory requ…
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
Cong Hao, Yao Chen, Xiaofan Zhang +4
High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators. To improve the overall solution quality…
DNNExplorer: A Framework for Modeling and Exploring a Novel Paradigm of FPGA-based DNN Accelerator
Xiaofan Zhang, Hanchen Ye, Junsong Wang +4
Existing FPGA-based DNN accelerators typically fall into two design paradigms. Either they adopt a generic reusable architecture to support different DNN networks but leave some pe…
Comprehensive assessment of error correction methods for high-throughput sequencing data
Yun Heo, Gowthami Manikandan, Anand Ramachandran +1
The advent of DNA and RNA sequencing has revolutionized the study of genomics and molecular biology. Next generation sequencing (NGS) technologies like Illumina, Ion Torrent, SOLiD…
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization
Cheng Gong, Yao Chen, Ye Lu +3
Quantization has been proven to be an effective method for reducing the computing and/or storage cost of DNNs. However, the trade-off between the quantization bitwidth and final ac…
HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy
Yao Chen, Xin Long, Jiong He +5
The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs an…