Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, MA
Academic Press
2021
|
| Ausgabe: | 1. edition |
| Schriftenreihe: | Advances in Computers
volume 122 |
| Schlagwörter: | |
| Abstract: | Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more.- Updates on new information on the architecture of GPU, NPU and DNN- Discusses In-memory computing, Machine intelligence and Quantum computing- Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance |
| Umfang: | xii, 402 Seiten Illustrationen |
| ISBN: | 9780128231234 |
Internformat
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| 505 | 8 | |a 1. Hardware accelerator systems for artificial intelligence and machine learning Shiho Kim 2. Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning Neha Gupta 3. Deep Learning with GPUs Won Woo Ro 4. Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures-Yuri Gordienko Yuri Gordienko 5. Architecture of NPU for DNN Kyuho Lee 6. Hardware Architecture for Convolutional Neural Network for Image Processing Vardhana M 7. FPGA based Neural Network Accelerators Joo-Young Kim 8. Energy-Efficient Deep Learning Inference on Edge Devices Massimo Poncino 9. Hardware accelerator systems for Embedded systems William Jinho Song 10. Generic Quantum Hardware Accelerators for Conventional systems Parth Bir 11. Music recommender system using Restricted Boltzmann Machine with Implicit Feedback Malaya Dutta Borah 12. Embedded system for Automated Monitoring in Agriculture and Healthcare Prashanta Kumar Das; | |
| 520 | 3 | |a Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more.- Updates on new information on the architecture of GPU, NPU and DNN- Discusses In-memory computing, Machine intelligence and Quantum computing- Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance | |
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| contents | 1. Hardware accelerator systems for artificial intelligence and machine learning Shiho Kim 2. Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning Neha Gupta 3. Deep Learning with GPUs Won Woo Ro 4. Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures-Yuri Gordienko Yuri Gordienko 5. Architecture of NPU for DNN Kyuho Lee 6. Hardware Architecture for Convolutional Neural Network for Image Processing Vardhana M 7. FPGA based Neural Network Accelerators Joo-Young Kim 8. Energy-Efficient Deep Learning Inference on Edge Devices Massimo Poncino 9. Hardware accelerator systems for Embedded systems William Jinho Song 10. Generic Quantum Hardware Accelerators for Conventional systems Parth Bir 11. Music recommender system using Restricted Boltzmann Machine with Implicit Feedback Malaya Dutta Borah 12. Embedded system for Automated Monitoring in Agriculture and Healthcare Prashanta Kumar Das; |
| ctrlnum | (ELiSA)ELiSA-9780128231234 (OCoLC)1250341935 (DE-599)BVBBV047281105 |
| edition | 1. edition |
| format | Book |
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| id | DE-604.BV047281105 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:14:57Z |
| institution | BVB |
| isbn | 9780128231234 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032684643 |
| oclc_num | 1250341935 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | xii, 402 Seiten Illustrationen |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Academic Press |
| record_format | marc |
| series | Advances in Computers |
| series2 | Advances in Computers |
| spellingShingle | Hardware accelerator systems for artificial intelligence and machine learning Advances in Computers 1. Hardware accelerator systems for artificial intelligence and machine learning Shiho Kim 2. Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning Neha Gupta 3. Deep Learning with GPUs Won Woo Ro 4. Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures-Yuri Gordienko Yuri Gordienko 5. Architecture of NPU for DNN Kyuho Lee 6. Hardware Architecture for Convolutional Neural Network for Image Processing Vardhana M 7. FPGA based Neural Network Accelerators Joo-Young Kim 8. Energy-Efficient Deep Learning Inference on Edge Devices Massimo Poncino 9. Hardware accelerator systems for Embedded systems William Jinho Song 10. Generic Quantum Hardware Accelerators for Conventional systems Parth Bir 11. Music recommender system using Restricted Boltzmann Machine with Implicit Feedback Malaya Dutta Borah 12. Embedded system for Automated Monitoring in Agriculture and Healthcare Prashanta Kumar Das; |
| title | Hardware accelerator systems for artificial intelligence and machine learning |
| title_auth | Hardware accelerator systems for artificial intelligence and machine learning |
| title_exact_search | Hardware accelerator systems for artificial intelligence and machine learning |
| title_full | Hardware accelerator systems for artificial intelligence and machine learning edited by Shiho Kim, School of Integrated Technology, Yonsei University, Seoul, South Korea; Ganesh Chandra Deka, Ministry of Skill Development and Entrepreneurship, New Delhi, India |
| title_fullStr | Hardware accelerator systems for artificial intelligence and machine learning edited by Shiho Kim, School of Integrated Technology, Yonsei University, Seoul, South Korea; Ganesh Chandra Deka, Ministry of Skill Development and Entrepreneurship, New Delhi, India |
| title_full_unstemmed | Hardware accelerator systems for artificial intelligence and machine learning edited by Shiho Kim, School of Integrated Technology, Yonsei University, Seoul, South Korea; Ganesh Chandra Deka, Ministry of Skill Development and Entrepreneurship, New Delhi, India |
| title_short | Hardware accelerator systems for artificial intelligence and machine learning |
| title_sort | hardware accelerator systems for artificial intelligence and machine learning |
| volume_link | (DE-604)BV002527667 |
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