375 citations · 457 across the 13 of their papers we have counts for
13 papers
A Cycle-Accurate Soft Error Vulnerability Analysis Framework for FPGA-based Designs
Eduardo Rhod, Behnam Ghavami, Zhenman Fang +1
Many aerospace and automotive applications use FPGAs in their designs due to their low power and reconfigurability requirements. Meanwhile, such applications also pose a high stand…
HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers
Peiyan Dong, Mengshu Sun, Alec Lu +8
While vision transformers (ViTs) have continuously achieved new milestones in the field of computer vision, their sophisticated network architectures with high computation and memo…
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…
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Jiaqing Zhang, Jie Lei, Weiying Xie +3
Accurately and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remains challenging. Most of the existing solutions primarily…
Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization
Zhengang Li, Mengshu Sun, Alec Lu +9
Vision transformers (ViTs) are emerging with significantly improved accuracy in computer vision tasks. However, their complex architecture and enormous computation/storage demand i…
TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-based FPGAs
Weikang Qiao, Licheng Guo, Zhenman Fang +2
The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available o…
BDFA: A Blind Data Adversarial Bit-flip Attack on Deep Neural Networks
Behnam Ghavami, Mani Sadati, Mohammad Shahidzadeh +2
Adversarial bit-flip attack (BFA) on Neural Network weights can result in catastrophic accuracy degradation by flipping a very small number of bits. A major drawback of prior bit f…
FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions
Behnam Ghavami, Mani Sadati, Zhenman Fang +1
Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars. In such DNN-based systems, error r…
Stealthy Attack on Algorithmic-Protected DNNs via Smart Bit Flipping
Behnam Ghavami, Seyd Movi, Zhenman Fang +1
Recently, deep neural networks (DNNs) have been deployed in safety-critical systems such as autonomous vehicles and medical devices. Shortly after that, the vulnerability of DNNs w…
SeaPlace: Process Variation Aware Placement for Reliable Combinational Circuits against SETs and METs
Kiarash Saremi, Hossein Pedram, Behnam Ghavami +3
Nowadays nanoscale combinational circuits are facing significant reliability challenges including soft errors and process variations. This paper presents novel process variation-aw…
Best-Effort FPGA Programming: A Few Steps Can Go a Long Way
Jason Cong, Zhenman Fang, Yuchen Hao +4
FPGA-based heterogeneous architectures provide programmers with the ability to customize their hardware accelerators for flexible acceleration of many workloads. Nonetheless, such…
Revisiting FPGA Acceleration of Molecular Dynamics Simulation with Dynamic Data Flow Behavior in High-Level Synthesis
Jason Cong, Zhenman Fang, Hassan Kianinejad +1
Molecular dynamics (MD) simulation is one of the past decade's most important tools for enabling biology scientists and researchers to explore human health and diseases. However, d…
ARAPrototyper: Enabling Rapid Prototyping and Evaluation for Accelerator-Rich Architectures
Yu-Ting Chen, Jason Cong, Zhenman Fang +2
Compared to conventional general-purpose processors, accelerator-rich architectures (ARAs) can provide orders-of-magnitude performance and energy gains and are emerging as one of t…