The field of AI is not new to researchers, as its foundations were established in the 1950s. After many decades of inattention, there has been a dramatic resurgence of interest in AI, fueled by a confluence of several factors. The benefits of decades of Dennard scaling and Moore's law miniaturization, coupled with the rise of highly distributed processing, have led to massively parallel systems well suited for handling big data. The widespread availability of big data, necessary for training AI algorithms, is another important factor. Finally, the greatly increased compute power and memory bandwidths have enabled deeper networks and new algorithms capable of accuracy rivaling that of human perception.
Already AI has shown success in many diverse areas, including finance (portfolio management, investment strategies), marketing, health care, transportation, gaming, defense, robotics, computer vision, education, search engines, online assistants, image/facial recognition, anomaly detection, spam filtering, online customer service, biometric sensors, and predictive maintenance, to name a few. Despite these remarkable advances, the human brain is still superior in many ways - including, notably, energy efficiency and one-shot learning - giving researchers new areas to explore. In summary, AI research and applications will continue with vigor in software, algorithms, and hardware accelerators. These exciting developments have also brought new questions of ethics and privacy, areas which must be studied in tandem with technological advances.
To continue the success story of AI, the AI Compute symposium was launched with the sponsorship of IBM, IEEE CAS and EDS for the first time. The aim of this publication is to compile all the materials presented by the renowned speakers in the symposium into a book format, serving as a learning tool for the audience.
This book contains two broad topics: general AI advances (chapters 1-5) and neuromorphic computing directions (chapters 6-9). Technical topics discussed in the book include:
1. Research Directions in AI algorithms and systems
2. An ARM perspective on hardware requirements and challenges for AI
3. The new Era of AI hardware
4. AI and the Opportunity for Unconventional Computing Platforms
5. Thermodynamic Computing
6. Brain-like cognitive engineering system
7. BRAINWAY and Nano - Abacus architecture: Brain-inspired Cognitive Computing using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design
8. Applying Lessons from Nature for Today's Computing Challenges
9. Emerging Memories - RRAM Fabric for Neuromorphic Computing Applications
Joshi, Rajiv: - Dr. Rajiv V. Joshi is a research staff member and key technical lead at T. J. Watson research center, IBM. He received his B.Tech I.I.T (Bombay, India), M.S (M.I.T) and Dr. Eng. Sc. (Columbia University). His novel interconnects processes and structures for aluminum, tungsten and copper technologies which are widely used in IBM for various technologies from sub-0.5μm to 14nm. He has led successfully predictive failure analytic techniques for yield prediction and also the technology-driven SRAM at IBM Server Group. He has extensively worked on novel memory designs. He commercialized these techniques. He received 3 Outstanding Technical Achievement (OTAs), 3 highest Corporate Patent Portfolio awards for licensing contributions, holds 60 invention plateaus and has over 235 US patents and over 354 including international patents. His interests are in in-memory computation, CNN, DNN accelerators and Quantum computing. He has authored and co-authored over 200 papers. He has given over 45 invited/keynote talks and given several Seminars. He is awarded prestigious IEEE Daniel Noble award for 2018. He received the Best Editor Award from IEEE TVLSI journal. He is recipient of 2015 BMM award. He is inducted into New Jersey Inventor Hall of Fame in Aug 2014 along with pioneer Nicola Tesla. He is a recipient of 2013 IEEE CAS Industrial Pioneer award and 2013 Mehboob Khan Award from Semiconductor Research Corporation. He is a member of IBM Academy of technology and a master inventor. He served as a Distinguished Lecturer for IEEE CAS and EDS society. He is currently Distinguished Lecturer for CEDA. He is IEEE, ISQED and World Technology Network fellow and distinguished alumnus of IIT Bombay. He serves in the Board of Governors for IEEE CAS as industrial liaison. He serves as an Associate Editor of TVLSI. He will and has served on committees of DAC 2019, AICAS 2019, ISCAS, ISLPED (Int. Symposium Low Power Electronic Design), IEEE VLSI design, IEEE CICC, IEEE Int. SOI conference, ISQED and Advanced Metallization Program committees. He initiated IBM CAS EDS symposium at IBM in 2017 and will continue into 2018 with Artificial Intelligence as the focal area. He served as a general chair for IEEE ISLPED. He is an industry liaison for universities as a part of the Semiconductor Research Corporation. Also he is in the industry liaison committee for IEEE CAS society.
Kumar, Arvind: - Arvind Kumar Arvind Kumar is a Research Staff Member and Manager at the IBM T.J. Watson Research Center in Yorktown Heights, NY. He has worked extensively on device design, characterization, and modeling for several IBM technologies. He has recently shifted his research interests to AI and heterogeneous integration. In addition to numerous invited talks and panel presentations, he has been involved with the IEEE Rebooting Computing initiative, serving as General Chair of ICRC 2017. He currently manages a team focused on next generation AI hardware. He holds a PhD in Electrical Engineering and Computer Science from MIT.
