@inproceedings{8982e834631444a48dd63d167ed66003,
title = "Emerging Trends in Multi-Accelerator and Distributed System for ML: Devices, Architectures, Tools and Applications",
abstract = "As the complexity and diversity of machine/deep learning models is increasing at a rapid pace, multi-accelerator and distributed systems are becoming a critical component of the machine learning (ML) stack. Besides efficient compute engines and communication mechanisms, these systems also require intelligent strategies for mapping workloads to accelerators and memory management to achieve high performance and energy efficiency, while meeting the demands for high-performance ML/AI systems. This article presents an overview of the emerging trends in multi-accelerator and distributed systems designed for handling complex AI-powered application workloads.",
keywords = "AI, Architecture, Deep Learning, Distributed System, DNN, Efficiency, Energy, Machine Learning, Memory, Multi-Accelerator",
author = "Muhammad Shafique",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 60th ACM/IEEE Design Automation Conference, DAC 2023 ; Conference date: 09-07-2023 Through 13-07-2023",
year = "2023",
doi = "10.1109/DAC56929.2023.10247935",
language = "English (US)",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 60th ACM/IEEE Design Automation Conference, DAC 2023",
}