AI Accelerator
AI accelerators are specialized hardware designed to significantly speed up and reduce the energy consumption of artificial intelligence computations. Current research focuses on optimizing performance for large language models and other deep neural networks, including exploring novel architectures like compute-in-memory and employing techniques such as weight sparsity and model compression to improve efficiency. This field is crucial for deploying AI in resource-constrained environments like mobile devices and edge computing, and for enabling the development and application of increasingly complex AI models. Improved efficiency and reduced latency are key objectives, driving innovation in both hardware design and software optimization strategies.
Papers
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill, Soritis S. Xantheas, Payel Das, Manuel Lopez Roldan, Mike Kraus, Hatem Helal, Sutanay Choudhury
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning
Andrey Ignatov, Anastasia Sycheva, Radu Timofte, Yu Tseng, Yu-Syuan Xu, Po-Hsiang Yu, Cheng-Ming Chiang, Hsien-Kai Kuo, Min-Hung Chen, Chia-Ming Cheng, Luc Van Gool