Efficient Machine Learning, Compilers, and Optimizations for Embedded Systems

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PubDate: Jun 2022

Teams: University of Illinois at Urbana-Champaign;Georgia Institute of Technology;University of California Irvine

Writers: Xiaofan Zhang, Yao Chen, Cong Hao, Sitao Huang, Yuhong Li, Deming Chen

PDF: Efficient Machine Learning, Compilers, and Optimizations for Embedded Systems

Abstract

Deep Neural Networks (DNNs) have achieved great success in a massive number of artificial intelligence (AI) applications by delivering high-quality computer vision, natural language processing, and virtual reality applications. However, these emerging AI applications also come with increasing computation and memory demands, which are challenging to handle especially for the embedded systems where limited computation/memory resources, tight power budgets, and small form factors are demanded. Challenges also come from the diverse application-specific requirements, including real-time responses, high-throughput performance, and reliable inference accuracy. To address these challenges, we will introduce a series of effective design methods in this book chapter to enable efficient algorithms, compilers, and various optimizations for embedded systems.

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