Processing-in-Memory for AI: From Circuits to Systems
Editat de Joo-Young Kim, Bongjin Kim, Tony Tae-Hyoung Kimen Limba Engleză Hardback – 10 iul 2022
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 430.78 lei 6-8 săpt. | |
| Springer International Publishing – 10 iul 2023 | 430.78 lei 6-8 săpt. | |
| Hardback (1) | 562.19 lei 6-8 săpt. | |
| Springer International Publishing – 10 iul 2022 | 562.19 lei 6-8 săpt. |
Preț: 562.19 lei
Preț vechi: 661.40 lei
-15% Nou
Puncte Express: 843
Preț estimativ în valută:
107.60€ • 113.51$ • 89.67£
107.60€ • 113.51$ • 89.67£
Carte tipărită la comandă
Livrare economică 02-16 ianuarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783030987800
ISBN-10: 3030987809
Pagini: 165
Ilustrații: V, 165 p. 134 illus., 99 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.43 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030987809
Pagini: 165
Ilustrații: V, 165 p. 134 illus., 99 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.43 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Introduction.- Background.- PIM Constraints and Approaches.- SRAM based PIM.- DRAM based PIM.- ReRAM based PIM.- PIM for AI Training.- PIM Systems.- Conclusion.
Notă biografică
Joo-Young Kim received the B.S., M.S., and Ph. D degree in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, in 2005, 2007, and 2010, respectively. He is currently an Assistant Professor in the School of Electrical Engineering at KAIST. He is also the Director of AI Semiconductor Systems (AISS) research center. His research interests span various aspects of hardware design including VLSI design, computer architecture, FPGA, domain-specific accelerators, hardware/software co-design, and agile hardware development. Before joining KAIST, he was a Senior Hardware Engineering Lead at Microsoft Azure, Redmond, WA, USA, working on hardware acceleration for its hyper-scale big data analytics platform named Azure Data Lake. Before that, he was one of the initial members of Catapult project at Microsoft Research, Redmond, WA, USA, where he deployed a fabric of FPGAs in datacenters to accelerate critical cloud services, such asmachine learning, data storage, and networking.
Dr. Kim is a recipient of the 2016 IEEE Micro Top Picks Award, the 2014 IEEE Micro Top Picks Award, the 2010 DAC/ISSCC Student Design Contest Award, the 2008 DAC/ISSCC Student Design Contest Award, and the 2006 A-SSCC Student Design Contest Award. He serves as Associate Editor for the IEEE Transactions on Circuits and Systems I: Regular Papers (2020-2021).
Bongjin Kim received BS and MS degrees from Pohang University of Science and Technology (POSTECH), Pohang, Korea, in 2004 and 2006, respectively and PhD degree from University of Minnesota, Minneapolis, MN, USA in 2014. After PhD, He worked on design techniques and methodologies for communication circuits and microarchitectures at Rambus and Stanford University as a senior staff and a postdoctoral research fellow. After working as an assistant professor at Nanyang Technological University in Singapore for three years (from 2017 to 2020), he joined Department of Electrical and Computer Engineering (ECE) at University of California, Santa Barbara.
From 2006 to 2010, he was with System LSI, Samsung Electronics, Yongin, South Korea. In 2012, he joined Wireless Business, Texas Instruments, Dallas, TX, USA as a SRC Summer Intern. He also joined Mixed-Signal Communication IC Design Group, IBM T.J. Watson Research Center as a Research Summer Intern in 2013. He was an Engineering Intern and a senior technical staff in Memory and Interface Division, Rambus Inc., Sunnyvale, CA, USA, from 2014 to 2016. Prof. Kim is the recipient of a Doctoral Dissertation Fellowship Award, a ISLPED Low Power Design Contest Award and an Intel/IBM/Catalyst Foundation CICC Student Award. His research works appeared in top circuit conferences and journals including ISSCC, VLSI symposium, CICC and JSSC. His current research focuses on memory-centric computing circuits/architecture using embedded memories for artificial intelligence, machine learning, and alternative computing solutions for solving combinatorial optimization problems.
Tony Tae-Hyoung Kim received the B.S. and M.S. degrees in electrical engineering from Korea University, Seoul, Korea, in 1999 and 2001, respectively. He received the Ph.D. degree in electrical and computer engineering from the University of Minnesota, Minneapolis, MN, USA in 2009. From 2001 to 2005, he worked for Samsung Electronics where he performed research on the design of high-speed SRAM memories, clock generators, and IO interface circuits. In 2007 ~ 2009 summer, he was with IBM T. J. Watson Research Center and Broadcom Corporation where he performed research on isolated NBTI/PBTI measurement circuits and SRAM mismatch measurement test structure, and battery backed memory design, respectively. In November 2009, he joined Nanyang Technological University where he is currently an associate professor. His current research interests include low power and high performance digital, mixed-mode, and memory circuitdesign, ultra-low voltage sub-threshold circuit design for energy efficiency, variation and aging tolerant circuits and systems, approximate computing, and circuit techniques for 3D ICs.
He received Best Demo Award at 2016 IEEE APCCAS, International Low Power Design Contest award at 2016 IEEE/ACM ISLPED, a best paper award at 2014 and 2011 ISOCC, 2008 AMD/CICC Student Scholarship Award, 2008 Departmental Research Fellowship from U. of Minnesota, 2008 IEEE DAC/ISSCC Student Design Contest Award, 2008 Samsung Humantec Thesis Award (Bronze Prize), 2005 ETRI Journal Paper of the Year Award, 2001 Samsung Humantec Thesis Award (Honor Prize), and 1999 Samsung Humantec Thesis Award (Silver Prize). He is an author/co-author of +160 journal and conference papers and holds 17 US and Korean patents. He serves as an Associate Editor of IEEE Transactions on Very Large Scale Integration (VLSI) Systems, IEEE Access, and IEIE Journal of Semiconductor Technology and Science (JSTS). He has also served as a technical committee member of various conferences such as IEEE Asian Solid-State Circuits Conference (A-SSCC), IEEE International Symposium on Circuits and Systems (ISCAS), IEEE Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), etc. He was the Chair of IEEE SSCS Singapore Chapter in 2015~2016. He is a senior member of IEEE.
Dr. Kim is a recipient of the 2016 IEEE Micro Top Picks Award, the 2014 IEEE Micro Top Picks Award, the 2010 DAC/ISSCC Student Design Contest Award, the 2008 DAC/ISSCC Student Design Contest Award, and the 2006 A-SSCC Student Design Contest Award. He serves as Associate Editor for the IEEE Transactions on Circuits and Systems I: Regular Papers (2020-2021).
Bongjin Kim received BS and MS degrees from Pohang University of Science and Technology (POSTECH), Pohang, Korea, in 2004 and 2006, respectively and PhD degree from University of Minnesota, Minneapolis, MN, USA in 2014. After PhD, He worked on design techniques and methodologies for communication circuits and microarchitectures at Rambus and Stanford University as a senior staff and a postdoctoral research fellow. After working as an assistant professor at Nanyang Technological University in Singapore for three years (from 2017 to 2020), he joined Department of Electrical and Computer Engineering (ECE) at University of California, Santa Barbara.
From 2006 to 2010, he was with System LSI, Samsung Electronics, Yongin, South Korea. In 2012, he joined Wireless Business, Texas Instruments, Dallas, TX, USA as a SRC Summer Intern. He also joined Mixed-Signal Communication IC Design Group, IBM T.J. Watson Research Center as a Research Summer Intern in 2013. He was an Engineering Intern and a senior technical staff in Memory and Interface Division, Rambus Inc., Sunnyvale, CA, USA, from 2014 to 2016. Prof. Kim is the recipient of a Doctoral Dissertation Fellowship Award, a ISLPED Low Power Design Contest Award and an Intel/IBM/Catalyst Foundation CICC Student Award. His research works appeared in top circuit conferences and journals including ISSCC, VLSI symposium, CICC and JSSC. His current research focuses on memory-centric computing circuits/architecture using embedded memories for artificial intelligence, machine learning, and alternative computing solutions for solving combinatorial optimization problems.
Tony Tae-Hyoung Kim received the B.S. and M.S. degrees in electrical engineering from Korea University, Seoul, Korea, in 1999 and 2001, respectively. He received the Ph.D. degree in electrical and computer engineering from the University of Minnesota, Minneapolis, MN, USA in 2009. From 2001 to 2005, he worked for Samsung Electronics where he performed research on the design of high-speed SRAM memories, clock generators, and IO interface circuits. In 2007 ~ 2009 summer, he was with IBM T. J. Watson Research Center and Broadcom Corporation where he performed research on isolated NBTI/PBTI measurement circuits and SRAM mismatch measurement test structure, and battery backed memory design, respectively. In November 2009, he joined Nanyang Technological University where he is currently an associate professor. His current research interests include low power and high performance digital, mixed-mode, and memory circuitdesign, ultra-low voltage sub-threshold circuit design for energy efficiency, variation and aging tolerant circuits and systems, approximate computing, and circuit techniques for 3D ICs.
He received Best Demo Award at 2016 IEEE APCCAS, International Low Power Design Contest award at 2016 IEEE/ACM ISLPED, a best paper award at 2014 and 2011 ISOCC, 2008 AMD/CICC Student Scholarship Award, 2008 Departmental Research Fellowship from U. of Minnesota, 2008 IEEE DAC/ISSCC Student Design Contest Award, 2008 Samsung Humantec Thesis Award (Bronze Prize), 2005 ETRI Journal Paper of the Year Award, 2001 Samsung Humantec Thesis Award (Honor Prize), and 1999 Samsung Humantec Thesis Award (Silver Prize). He is an author/co-author of +160 journal and conference papers and holds 17 US and Korean patents. He serves as an Associate Editor of IEEE Transactions on Very Large Scale Integration (VLSI) Systems, IEEE Access, and IEIE Journal of Semiconductor Technology and Science (JSTS). He has also served as a technical committee member of various conferences such as IEEE Asian Solid-State Circuits Conference (A-SSCC), IEEE International Symposium on Circuits and Systems (ISCAS), IEEE Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), etc. He was the Chair of IEEE SSCS Singapore Chapter in 2015~2016. He is a senior member of IEEE.
Textul de pe ultima copertă
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).
- Provides first book that describes the processing-in-memory (PIM) technology thoroughly from architectures to circuits;
- Describes architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM);
- Focuses on feasible PIM solutions that can be implemented and used in real systems.
Caracteristici
Provides first book that describes the processing-in-memory (PIM) technology thoroughly from architectures to circuits Describes architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM) Focuses on feasible PIM solutions that can be implemented and used in real systems