4 citations · 7 across the 12 of their papers we have counts for
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cs.AR2024
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…
cs.AR2024★ 4 cited
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…
cs.PL2024
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…