Solid State Circuits Society

Barring technologies surprises (such as the discovery of a perfect nanometer switch), alternative design strategies may be necessary if continued scaling of functionality in terms of size and energy is to be obtained. Neuro-inspired computing is one possible direction to be considered. Over the past decade, the brain has been receiving a lot of attention (e.g. the BRAIN initiatives in the US and Europe) – mostly from a mapping and an understanding perspective. The brain is an amazingly complex and efficient machine. While it may not be considered “general purpose” in terms of its computational capabilities, it performs a set of functions such as feature extraction, classification, synthesis, recognition, learning, and higher-order decision-making amazingly well. Carver Mead already realized this in the late 1980’s – yet the technological landscape at that time was not amenable to make neuromorphic computing an attractive alternative.

Today, it is realized that neuro-inspired computing may be a perfect match to the properties of the emerging nano-scale devices (such as 3D integration, carbon and spin devices, non-volatile memory cells such as RRAM, etc): it thrives on randomness and variability, processing is performed in the continuous or discrete domains, and massive parallelism, major redundancy and adaptivity are of essence. Computational paradigms inspired by neural information processing hence may lead to energy-efficient, low-cost, dense and/or reliable implementations of the functions the brain excels at. In this presentation, we will explore various means on how the interaction between neuroscience and information technology may lead to an exciting future.

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