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, i.e., 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 ...
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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, i.e., 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.
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Add this copy of Towards Heterogeneous Multi-Core Systems-on-Chip for to cart. $132.50, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Santa Clarita, CA, UNITED STATES, published 2023 by Springer.
All Editions of Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning: Journey from Single-core Acceleration to Multi-core Heterogeneous Systems
