5 citations · 13 across the 6 of their papers we have counts for
6 papers
ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation
Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin +3
Recent years have witnessed the growing popularity of domain-specific accelerators (DSAs), such as Google's TPUs, for accelerating various applications such as deep learning, searc…
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…
Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks
Atefeh Sohrabizadeh, Yunsheng Bai, Yizhou Sun +1
High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transf…
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…
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…
AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators
Atefeh Sohrabizadeh, Cody Hao Yu, Min Gao +1
Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized computing, but the fact that FPGAs are hard to program creates a steep learning curve for softw…