4 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
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…