44 citations · 67 across the 5 of their papers we have counts for
5 papers
TAPA-CS: Enabling Scalable Accelerator Design on Distributed HBM-FPGAs
Neha Prakriya, Yuze Chi, Suhail Basalama +2
Despite the increasing adoption of Field-Programmable Gate Arrays (FPGAs) in compute clouds, there remains a significant gap in programming tools and abstractions which can leverag…
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan +7
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability…
Democratizing Domain-Specific Computing
Yuze Chi, Weikang Qiao, Atefeh Sohrabizadeh +2
In the past few years, domain-specific accelerators (DSAs), such as Google's Tensor Processing Units, have shown to offer significant performance and energy efficiency over general…
TAPA: A Scalable Task-Parallel Dataflow Programming Framework for Modern FPGAs with Co-Optimization of HLS and Physical Design
Licheng Guo, Yuze Chi, Jason Lau +9
In this paper, we propose TAPA, an end-to-end framework that compiles a C++ task-parallel dataflow program into a high-frequency FPGA accelerator. Compared to existing solutions, T…
Callipepla: Stream Centric Instruction Set and Mixed Precision for Accelerating Conjugate Gradient Solver
Linghao Song, Licheng Guo, Suhail Basalama +3
The continued growth in the processing power of FPGAs coupled with high bandwidth memories (HBM), makes systems like the Xilinx U280 credible platforms for linear solvers which oft…