Нүүр зураг

Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning Journey from Single-core Acceleration to Multi-core Heterogeneous Systems /

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 hard...

Бүрэн тодорхойлолт

-д хадгалсан:
Номзүйн дэлгэрэнгүй
Үндсэн зохиолчид: Jain, Vikram (Зохиогч), Verhelst, Marian (Зохиогч)
Байгууллагын зохиогч: SpringerLink (Online service)
Формат: Цахим Цахим ном
Хэл сонгох:англи
Хэвлэсэн: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Хэвлэл:1st ed. 2024.
Нөхцлүүд:
Electronic circuits.
Embedded computer systems.
Machine learning.
Microprocessors.
Computer architecture.
Electronic Circuits and Systems.
Embedded Systems.
Machine Learning.
Processor Architectures.
Онлайн хандалт:https://doi.org/10.1007/978-3-031-38230-7
Шошгууд: Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
Агуулга:
  • Chapter 1: Introduction
  • Chapter 2 Algorithmic Background for Machine Learning
  • Chapter 3 Scoping the Landscape of (Extreme) Edge Machine Learning Processors
  • Chapter 4 Hardware-Software Co-optimization through Design Space Exploration
  • Chapter 5 Energy Efficient Single-core Hardware Acceleration
  • Chapter 6 TinyVers: A Tiny Versatile All-Digital Heterogeneous Multi-core System-on-Chip
  • Chapter 7 DIANA: Digital and ANAlog Heterogeneous Multi-core System-on-Chip
  • Chapter 8 Networks-on-chip to Enable Large-scale Multi-core ML Acceleration
  • Chapter 9 Conclusion.

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