24 citations · 59 across the 4 of their papers we have counts for
6 papers
Particle Mesh Ewald for Molecular Dynamics in OpenCL on an FPGA Cluster
Lawrence C. Stewart, Carlo Pascoe, Brian W. Sherman +2
Molecular Dynamics (MD) simulations play a central role in physics-driven drug discovery. MD applications often use the Particle Mesh Ewald (PME) algorithm to accelerate electrosta…
Secret Sharing MPC on FPGAs in the Datacenter
Pierre-Francois Wolfe, Rushi Patel, Robert Munafo +2
Multi-Party Computation (MPC) is a technique enabling data from several sources to be used in a secure computation revealing only the result while protecting the original data, fac…
CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks
Runbin Shi, Peiyan Dong, Tong Geng +6
Recurrent neural networks (RNNs) have been widely adopted in temporal sequence analysis, where realtime performance is often in demand. However, RNNs suffer from heavy computationa…
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
Tong Geng, Ang Li, Runbin Shi +8
Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio. In many applications, however, information and their relationships are bett…
Fully Integrated On-FPGA Molecular Dynamics Simulations
Chen Yang, Tong Geng, Tianqi Wang +8
The implementation of Molecular Dynamics (MD) on FPGAs has received substantial attention. Previous work, however, has consisted of either proof-of-concept implementations of compo…
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
Tong Geng, Tianqi Wang, Ang Li +2
Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is gen…