Very large-scale neuromorphic systems for biological signal processing

Catthoor, F., Mitra, S., Das, A. and Schaafsma, S. (2017) Very large-scale neuromorphic systems for biological signal processing. In: Mitra, S. and Cumming, D. R.S. (eds.) CMOS Circuits for Biological Sensing and Processing. Springer, pp. 315-340. ISBN 9783319677224 (doi: 10.1007/978-3-319-67723-1_13)

Abstract

This chapter is a white paper describing a platform for scaled-up neuromorphic systems to ‘human brain size’ complexity. Such a system will be necessary for massive search and analysis tasks while interacting with biological data. This system would consist of similar number of neurons and synapses as in an adult human brain. One of the largest bottlenecks is the huge synaptic complexity that would result from connecting billions of neurons. The purpose of this chapter is to describe a feasible architecture that could handle the enormous communication bandwidth necessary for such a large-scale neuromorphic system. The proposed approach is grounded in the assumption that we would only be able to appreciate the utility of a neuromorphic system when it is somewhat similar to the human brain in terms of energy consumption and size. Inspired by the recent advancements in SoC architecture, a novel scalable intercluster communication network is proposed here. A particularly useful instantiation of this occurs for the global synaptic communication, interconnecting the local clusters of synapse arrays. The core of the proposed solution is a novel switching architecture in the CMOS back end of line (BEOL) that is expected to be extremely power efficient. In contrast to a fixed predefined bus that is shared over all connected local clusters, the proposed solution will allow a multitude of dedicated point-to-point connections that can be switched dynamically.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Mitra, Dr Srinjoy
• Authors: Catthoor, F., Mitra, S., Das, A., and Schaafsma, S.
• College/School: College of Science and Engineering > School of Engineering
• Publisher: Springer
• ISBN: 9783319677224