Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning: Journey from Single-core Acceleration to Multi-core Heterogeneous Systems
by Vikram Jain and Marian Verhelst
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Fulfillment optionsTowards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning
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.
Details
- Publisher
- Springer Nature Switzerland
- Authors
- Vikram Jain, Marian Verhelst
- Format
- Paperback
- Published
- Sep 18, 2024
- ISBN-13
- 9783031382321
- Pages
- 186
- Printing
- Printed on demand
Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning for groups: pricing
Volume pricing applies automatically in your cart. No codes, no account needed.
| Copies | Price per copy | You save |
|---|---|---|
| 25 – 49 | $96.79 | 12% off list |
| 50 – 99 | $95.69 | 13% off list |
| 100+ | $94.59 | 14% off list |
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Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning: Journey from Single-core Acceleration to Multi-core Heterogeneous Systems
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