Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning

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Journey from Single-core Acceleration to Multi-core Heterogeneous Systems
Author(s): Vikram Jain, Marian Verhelst
Format: Paperback
Publisher: Springer International Publishing AG, Switzerland
Imprint: Springer International Publishing AG
ISBN-13: 9783031382321, 978-3031382321

Synopsis

This book explores and motivates the need for building homogeneous and heterogeneous multi-core systems for machine learning to enable flexibility and energy-efficiency. Coverage focuses on a key aspect of the challenges of (extreme-)edge-computing, [url] design of energy-efficient and flexible hardware architectures, and hardware-software co-optimization strategies to enable early design space exploration of hardware architectures. The authors investigate possible design solutions for building single-core specialized hardware accelerators for machine learning and motivates the need for building homogeneous and heterogeneous multi-core systems to enable flexibility and energy-efficiency. The advantages of scaling to heterogeneous multi-core systems are shown through the implementation of multiple test chips and architectural optimizations.