4 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
F-BFQ: Flexible Block Floating-Point Quantization Accelerator for LLMs
Jude Haris, José Cano
Large Language Models (LLMs) have become increasingly prominent for daily tasks, from improving sound-totext translation to generating additional frames for the latest video games.…
Accelerating Transposed Convolutions on FPGA-based Edge Devices
Jude Haris, José Cano
Transposed Convolutions (TCONV) enable the up-scaling mechanism within generative Artificial Intelligence (AI) models. However, the predominant Input-Oriented Mapping (IOM) method…
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…