89 citations · 187 across the 12 of their papers we have counts for
12 papers
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…
SASA: A Scalable and Automatic Stencil Acceleration Framework for Optimized Hybrid Spatial and Temporal Parallelism on HBM-based FPGAs
Xingyu Tian, Zhifan Ye, Alec Lu +3
Stencil computation is one of the fundamental computing patterns in many application domains such as scientific computing and image processing. While there are promising studies th…
SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation
Atefeh Sohrabizadeh, Yuze Chi, Jason Cong
While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving…
Serpens: A High Bandwidth Memory Based Accelerator for General-Purpose Sparse Matrix-Vector Multiplication
Linghao Song, Yuze Chi, Licheng Guo +1
Sparse matrix-vector multiplication (SpMV) multiplies a sparse matrix with a dense vector. SpMV plays a crucial role in many applications, from graph analytics to deep learning. Th…
Pyxis: An Open-Source Performance Dataset of Sparse Accelerators
Linghao Song, Yuze Chi, Jason Cong
Specialized accelerators provide gains of performance and efficiency in specific domains of applications. Sparse data structures or/and representations exist in a wide range of app…
Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication
Linghao Song, Yuze Chi, Atefeh Sohrabizadeh +3
Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of applications, including scientific computing, graph processing, and deep learning. Architec…
When HLS Meets FPGA HBM: Benchmarking and Bandwidth Optimization
Young-kyu Choi, Yuze Chi, Jie Wang +2
With the recent release of High Bandwidth Memory (HBM) based FPGA boards, developers can now exploit unprecedented external memory bandwidth. This allows more memory-bounded applic…
Extending High-Level Synthesis for Task-Parallel Programs
Yuze Chi, Licheng Guo, Jason Lau +3
C/C++/OpenCL-based high-level synthesis (HLS) becomes more and more popular for field-programmable gate array (FPGA) accelerators in many application domains in recent years, thank…
Rapid Cycle-Accurate Simulator for High-Level Synthesis
Yuze Chi, Young-kyu Choi, Jason Cong +1
A large semantic gap between the high-level synthesis (HLS) design and the low-level (on-board or RTL) simulation environment often creates a barrier for those who are not FPGA exp…
NXgraph: An Efficient Graph Processing System on a Single Machine
Yuze Chi, Guohao Dai, Yu Wang +3
Recent studies show that graph processing systems on a single machine can achieve competitive performance compared with cluster-based graph processing systems. In this paper, we pr…