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Processing-in-Memory for AI: From Circuits to Systems
HKD 605
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產品詳情
- This book provides a comprehensive introduction to processing-in-memory (PIM) technology, from its architectures to circuits implementations on multiple memory types and describes how it can be a viable computer architecture in the era of AI and big data. The authors summarize the challenges of AI hardware systems, processing-in-memory (PIM) constraints and approaches to derive system-level requirements for a practical and feasible PIM solution. The presentation focuses on feasible PIM solutions that can be implemented and used in real systems, including architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM).
| Publisher | Springer |
| Publication date | July 9, 2022 |
| Language | English |
| File size | 41.0 MB |
| Screen Reader | Supported |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 268 pages |
| ISBN-13 | 978-3030987817 |
| Page Flip | Enabled |
| Item Weight | 1 lbs (450 grams) |
產品敘述
Processing-in-Memory for AI: From Circuits to Systems
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Design Editorial Review
Processing-in-Memory for AI: From Circuits to Systems is a comprehensive exploration of the cutting-edge integration of memory processing in artificial intelligence technologies. Published by Springer on July 9, 2022, this 268-page book delves into innovative concepts that enhance computational efficiency, particularly important in the realm of AI. With enhanced typesetting supported for better readability, the book is designed for both researchers and practitioners and is accessible to screen readers. The content aligns with current technological advancements and addresses practical applications, making it a valuable resource in the evolving field of AI.
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優點
- Comprehensive resource for AI and memory processing
- Enhanced typesetting for improved readability
- Supports screen readers for accessibility
- In-depth exploration of innovative concepts
- Includes practical applications in AI
缺點
- Not suitable for beginners unfamiliar with the topic
Product Price History
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