This book presents recent advances towards the goal of enabling
efficient implementation of machine learning models on
resource-constrained systems, covering different application
domains. The focus is on presenting interesting and new use cases
of applying machine learning to innovative application domains,
exploring the efficient hardware design of efficient machine
learning accelerators, memory optimization techniques, illustrating
model compression and neural architecture search techniques for
energy-efficient and fast execution on resource-constrained
hardware platforms, and understanding hardware-software codesign
techniques for achieving even greater energy, reliability, and
performance benefits.
General
| Imprint: |
Springer International Publishing AG
|
| Country of origin: |
Switzerland |
| Release date: |
October 2023 |
| First published: |
2023 |
| Editors: |
Sudeep Pasricha
• Muhammad Shafique
|
| Dimensions: |
235 x 155mm (L x W) |
| Format: |
Hardcover
|
| Pages: |
592 |
| Edition: |
1st ed. 2023 |
| ISBN-13: |
978-3-03-119567-9 |
| Categories: |
Books
|
| LSN: |
3-03-119567-1 |
| Barcode: |
9783031195679 |
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