Joshi R., Ziegler M., Kumar A., Alarcon E. From Artificial Intelligence to Brain Intelligence

Gistrup: River Publishers, 2022. — 209 p.Research in Artificial Intelligence (AI) is not new, it has been around since the 1950s. 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 defense 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 for 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.Research Directions in Al Algorithms and Systems.
An Arm Perspective on Hardware Requirements and Challenges for Al.
The New Era of Al Hardware.
Al and the Opportunity for Unconventional Computing Platforms.
Thermodynamic Computing.
Brain-like Cognitive Engineering Systems.
BRAINWAY and nano-Abacus Architecture: Brain-inspired Cognitive Computing Using Energy Efficient Physical Computational Structures, algorithms, and Architecture Co-Design.
The Loihi Neuromorphic Research Chip.
RRAM Fabric for Neuromorphic Computing Applications.