Arvind Kumar

Eduard Alarcon

Rajiv Joshi

Matt Ziegler

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.

AI Compute Symposium 218

From Artificial Intelligence to Brain Intelligence

Elena Blokhina

Dimitri Galayko

Abdelali El Aroudi

This book is a single-source guide to nonlinearity and nonlinear techniques in energy harvesting, with a focus on vibration energy harvesters for micro and nanoscale applications. The authors demonstrate that whereas nonlinearity was avoided as an undesirable phenomenon in early energy harvesters, now it can be used as an essential part of these systems. Readers will benefit from an overview of nonlinear techniques and applications, as well as deeper insight into methods of analysis and modeling of energy harvesters, employing different nonlinearities. The role of nonlinearity due to different aspects of an energy harvester is discussed, including nonlinearity due to mechanical-to-electrical conversion, nonlinearity due to conditioning electronic circuits, nonlinearity due to novel materials (e.g., graphene), etc. Coverage includes tutorial introductions to MEMS and NEMS technology, as well as a wide range of applications, such as nonlinear oscillators and transducers for energy harvesters and electronic conditioning circuits for effective energy processing.

Micro- and Nanoscale Applications

Nonlinearity in Energy Harvesting Systems

Eduard Alarcon

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