305 citations · 669 across the 22 of their papers we have counts for
6 papers · 1 filter
G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency
Yongan Zhang, Haoran You, Yonggan Fu +3
Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA) method for graph-based learning tasks. However, it still remains prohibitively challenging to inference GNN…
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search
Hongwu Peng, Shiyang Chen, Zhepeng Wang +9
Molecular similarity search has been widely used in drug discovery to identify structurally similar compounds from large molecular databases rapidly. With the increasing size of ch…
Binary Complex Neural Network Acceleration on FPGA
Hongwu Peng, Shanglin Zhou, Scott Weitze +9
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown effi…
CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression
Chengming Zhang, Sian Jin, Tong Geng +3
As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounde…
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Boyuan Feng, Yuke Wang, Tong Geng +2
Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activati…
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
Samuel A. Stein, Betis Baheri, Daniel Chen +5
In the past decade, remarkable progress has been achieved in deep learning related systems and applications. In the post Moore's Law era, however, the limit of semiconductor fabric…