4 citations · 4 across the 4 of their papers we have counts for
5 papers
FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining
Rappy Saha, Nima Amirafshar, Jude Haris +2
Approximate multipliers can reduce hardware area and energy consumption in Deep Neural Network (DNN) inference; however, they introduce computational errors. Assessing the accuracy…
FlexViT: A Flexible FPGA-based Accelerator for Edge Vision Transformers
Hubert Dymarkowski, Xingjian Fu, Rappy Saha +2
Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT mod…
PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
Rappy Saha, Jude Haris, Nicolas Bohm Agostini +2
Power-of-two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit-shift operations for inference. Prior work has s…
Accelerating PoT Quantization on Edge Devices
Rappy Saha, Jude Haris, José Cano
Non-uniform quantization, such as power-of-two (PoT) quantization, matches data distributions better than uniform quantization, which reduces the quantization error of Deep Neural…
Designing Efficient LLM Accelerators for Edge Devices
Jude Haris, Rappy Saha, Wenhao Hu +1
The increase in open-source availability of Large Language Models (LLMs) has enabled users to deploy them on more and more resource-constrained edge devices to reduce reliance on n…