From Artificial Intelligence to Brain Intelligence

✍ Scribed by Rajiv Joshi, Matt Ziegler, Arvind Kumar, Eduard Alarcon

• Publisher: River Publishers
• Year: 2022
• Tongue: English
• Leaves: 209
• Series: Tutorials in Circuits and Systems
• Category: Library

✦ Synopsis

Research in Artificial Intelligence (AI) is not new, it has been around since 1950’s. AI resurfaced at that time while Moore’s law was on an aggressive path of scaling, with the transformation of NMOS and later bipolar technology to CMOS for high performance, low power as well as low cost applications.Several breakthroughs in the electronics industry helped to push Moore’s law in chip miniaturization along with increased computing power (parallel and distributed processing) and memory bandwidth. Once this paradigm shift occurred it naturally opened doors for AI as it required big data manipulations, and thus AI could thrive again. AI has already shown success in industries such as finance, marketing, health care, transportation, gaming, education and the defence and space, to name but a few.The human brain amazingly has a memory in the order of millions of digital bits, however it cannot compete with machines for data crunching and speed. Thus tomorrow’s world will be a World of Wonders of Artificial Intelligence (WOW- AI), to compensate the computational limitations of human beings. In short, AI research and applications will continue to grow with the development of software, algorithms and hardware accelerators.To continue the development of AI, an advanced AI Compute Symposium was launched with the sponsorship of IBM, IEEE CAS and EDS, from which this book came. Overall, the book covers two broad topics: general AI advances, and applications to neuromorphic computing.

✦ Table of Contents

Cover
Title
Copyright
Series
Table of contents
Introduction
1. Research Directions in Al Algorithms and Systems
2. An Arm Perspective on Hardware Requirements and Challenges for Al
3. The New Era of Al Hardware
4. Al and the Opportunity for Unconventional Computing Platforms
5. Thermodynamic Computing
6. Brain-like Cognitive Engineering Systems
7. BRAINWAY and nano-Abacus Architecture: Brain-inspired Cognitive Computing Using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design
8. The Loihi Neuromorphic Research Chip
9. RRAM Fabric for Neuromorphic Computing Applications
About the Editors
About the Authors