title: Network-driven design principles for neuromorphic systems.
abstract: Synaptic connectivity is typically the most resource-demanding part of neuromorphic systems. Commonly, the architecture of these systems is chosen mainly on technical considerations. As a consequence, the potential for optimization arising from the inherent constraints of connectivity models is left unused. In this article, we develop an alternative, network-driven approach to neuromorphic architecture design. We describe methods to analyse performance of existing neuromorphic architectures in emulating certain connectivity models. Furthermore, we show step-by-step how to derive a neuromorphic architecture from a given connectivity model. For this, we introduce a generalized description for architectures with a synapse matrix, which takes into account shared use of circuit components for reducing total silicon area. Architectures designed with this approach are fitted to a connectivity model, essentially adapting to its connection density. They are guaranteeing faithful reproduction of the model on chip, while requiring less total silicon area. In total, our methods allow designers to implement more area-efficient neuromorphic systems and verify usability of the connectivity resources in these systems.
authors.affiliation: Chair for Highly Parallel VLSI Systems and Neuromorphic Circuits, Department of Electrical Engineering and Information Technology, Technische Universität Dresden Dresden, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Partzsch
authors.affiliation: Chair for Highly Parallel VLSI Systems and Neuromorphic Circuits, Department of Electrical Engineering and Information Technology, Technische Universität Dresden Dresden, Germany.
authors.forename: Rene
authors.initials: R
authors.lastname: Schüffny
keywords: Rent's rule; mapping quality; neuromorphic architectures; synaptic connectivity; system design