101 citations · 177 across the 9 of their papers we have counts for
12 papers
Fine-Tuning GPT-5 for GPU Kernel Generation
Ali Tehrani, Yahya Emara, Essam Wissam +4
Developing efficient GPU kernels is essential for scaling modern AI systems, yet it remains a complex task due to intricate hardware architectures and the need for specialized opti…
Fast Sampling Through The Reuse Of Attention Maps In Diffusion Models
Rosco Hunter, Łukasz Dudziak, Mohamed S. Abdelfattah +3
Text-to-image diffusion models have demonstrated unprecedented capabilities for flexible and realistic image synthesis. Nevertheless, these models rely on a time-consuming sampling…
BLOX: Macro Neural Architecture Search Benchmark and Algorithms
Thomas Chun Pong Chau, Łukasz Dudziak, Hongkai Wen +2
Neural architecture search (NAS) has been successfully used to design numerous high-performance neural networks. However, NAS is typically compute-intensive, so most existing appro…
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
Hongxiang Fan, Thomas Chau, Stylianos I. Venieris +5
Attention-based neural networks have become pervasive in many AI tasks. Despite their excellent algorithmic performance, the use of the attention mechanism and feed-forward network…
Temporal Kernel Consistency for Blind Video Super-Resolution
Lichuan Xiang, Royson Lee, Mohamed S. Abdelfattah +2
Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation. These models are able to accu…
Zero-Cost Operation Scoring in Differentiable Architecture Search
Lichuan Xiang, Łukasz Dudziak, Mohamed S. Abdelfattah +3
We formalize and analyze a fundamental component of differentiable neural architecture search (NAS): local "operation scoring" at each operation choice. We view existing operation…
Zero-Cost Proxies for Lightweight NAS
Mohamed S. Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak +1
Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models n…
Iterative Compression of End-to-End ASR Model using AutoML
Abhinav Mehrotra, Łukasz Dudziak, Jinsu Yeo +9
Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research hav…
Journey Towards Tiny Perceptual Super-Resolution
Royson Lee, Łukasz Dudziak, Mohamed Abdelfattah +4
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks.…
BRP-NAS: Prediction-based NAS using GCNs
Łukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah +3
Neural architecture search (NAS) enables researchers to automatically explore broad design spaces in order to improve efficiency of neural networks. This efficiency is especially i…
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator
Mohamed S. Abdelfattah, Łukasz Dudziak, Thomas Chau +3
Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present,…
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning
Łukasz Dudziak, Mohamed S. Abdelfattah, Ravichander Vipperla +2
End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses re…