| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046197383 | |
| 005 | 20250414174551 | |
| 008 | 250404r20222020sz a b 000 0 eng d | |
| 020 | ▼a 9783031006388 (pbk.) | |
| 020 | ▼a 9783031000638 (hbk.) | |
| 020 | ▼z 9783031017667 (ebook) | |
| 022 | ▼z 1935-3243 (electronic) | |
| 035 | ▼a (KERIS)BIB000017032497 | |
| 040 | ▼a 211046 ▼c 211046 ▼d 211009 | |
| 082 | 0 4 | ▼a 006.32 ▼2 23 |
| 084 | ▼a 006.32 ▼2 DDCK | |
| 090 | ▼a 006.32 ▼b E27a | |
| 245 | 0 0 | ▼a Efficient processing of deep neural networks / ▼c Vivienne Sze, Yu-Hsin Chen, and Tien-Ju Yang, Joel S. Emer. |
| 260 | ▼a Cham : ▼b Springer, ▼c 2022. | |
| 300 | ▼a xxi, 319 p. : ▼b col. ill. ; ▼c 24 cm. | |
| 490 | 1 | ▼a Synthesis lectures of computer architecture, ▼x 1935-3235 ; ▼v 50 |
| 500 | ▼a Reprint of original edition ⓒMorgan & Claypool 2020. | |
| 504 | ▼a Includes bibliographical references (p. 283-316). | |
| 650 | 0 | ▼a Neural networks (Computer science). |
| 650 | 0 | ▼a Machine learning. |
| 700 | 1 | ▼a Sze, Vivienne. |
| 700 | 1 | ▼a Chen, Yu-Hsin. |
| 700 | 1 | ▼a Yang, Tien-Ju. |
| 700 | 1 | ▼a Emer, Joel S., ▼d 1954-. |
| 830 | 0 | ▼a Synthesis lectures of computer architecture ; ▼v 50. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.32 E27a | 등록번호 121268990 (3회 대출) | 도서상태 대출중 | 반납예정일 2026-04-07 | 예약 예약가능 | 서비스 |
컨텐츠정보
책소개
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics?such as energy-efficiency, throughput, and latency?without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems.
The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
정보제공 :
목차
Preface.- Acknowledgments.- Introduction.- Overview of Deep Neural Networks.- Key Metrics and Design Objectives.- Kernel Computation.- Designing DNN Accelerators.- Operation Mapping on Specialized Hardware.- Reducing Precision.- Exploiting Sparsity.- Designing Efficient DNN Models.- Advanced Technologies.- Conclusion.- Bibliography.- Authors' Biographies.
정보제공 :