4 citations · 4 across the 8 of their papers we have counts for
9 papers
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…
ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks
Wenhao Hu, Paul Henderson, José Cano
Pruning is a widely used method for compressing Deep Neural Networks (DNNs), where less relevant parameters are removed from a DNN model to reduce its size. However, removing param…
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks
Idris Zakariyya, Ferheen Ayaz, Mounia Kharbouche-Harrari +4
Reducing the memory footprint of Machine Learning (ML) models, especially Deep Neural Networks (DNNs), is imperative to facilitate their deployment on resource-constrained edge dev…
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations
Wenhao Hu, Paul Henderson, José Cano
Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial o…
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…
Data Transfer Optimizations for Host-CPU and Accelerators in AXI4MLIR
Jude Haris, Nicolas Bohm Agostini, Antonino Tumeo +2
As custom hardware accelerators become more prevalent, it becomes increasingly important to automatically generate efficient host-driver code that can fully leverage the capabiliti…