The field of AI is not new to researchers, as itsfoundations were established in the 1950s. After many decades of inattention, therehas been a dramatic resurgence of interest in AI, fueled by a confluence ofseveral factors. The benefits of decades of Dennard scaling and Moore’s lawminiaturization, coupled with the rise of highly distributed processing, haveled to massively parallel systems well suited for handling big data. Thewidespread availability of big data, necessary for training AI algorithms, isanother important factor. Finally, the greatly increased compute power andmemory bandwidths have enabled deeper networks and new algorithms capable ofaccuracy rivaling that of human perception.
Already AI has shown success in many diverseareas, including finance (portfolio management, investment strategies),marketing, health care, transportation,gaming, defense, robotics, computer vision, education, search engines, onlineassistants, image/facial recognition, anomaly detection, spam filtering, onlinecustomer service, biometric sensors, and predictive maintenance, to name a few.Despite these remarkable advances, the human brain is still superior in manyways – including, notably, energy efficiency and one-shot learning – givingresearchers new areas to explore. In summary, AI research and applications willcontinue with vigor in software, algorithms, and hardware accelerators. Theseexciting 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 thefirst time. The aim of this publication is to compile all the materialspresented by the renowned speakers in the symposium into a book format, servingas a learning tool for the audience.
This bookcontains two broad topics: general AI advances (chapters 1-5) and neuromorphiccomputing directions (chapters 6-9). Technical topics discussed in the bookinclude:
- Research Directions in AI algorithms and systems
- An ARM perspective on hardware requirements and challenges for AI
- The new Era of AI hardware
- AI and the Opportunity for Unconventional Computing Platforms
- Thermodynamic Computing
- Brain-like cognitive engineering system
- BRAINWAY and Nano - Abacus architecture: Brain-inspired Cognitive Computing using Energy Efficient Physical Computational Structures, Algorithms and Architecture Co-Design
- Applying Lessons from Nature for Today’s Computing Challenges
- Emerging Memories - RRAM Fabric for Neuromorphic Computing Applications
The field of AI is not new to researchers, as itsfoundations were established in the 1950s. After many decades of inattention, therehas been a dramatic resurgence of interest in AI, fueled by a confluence ofseveral factors. The benefits of decades of Dennard scaling and Moore’s lawminiaturization, coupled with the rise of highly distributed processing, haveled to massively parallel systems well suited for handling big data. Thewidespread availability of big data, necessary for training AI algorithms, isanother important factor. Finally, the greatly increased compute power andmemory bandwidths have enabled deeper networks and new algorithms capable ofaccuracy rivaling that of human perception.
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