title: An Experimentation Platform for On-Chip Integration of Analog Neural Networks: A Pathway to Trusted and Robust Analog/RF ICs.
abstract: We discuss the design of an experimentation platform intended for prototyping low-cost analog neural networks for on-chip integration with analog/RF circuits. The objective of such integration is to support various tasks, such as self-test, self-tuning, and trust/aging monitoring, which require classification of analog measurements obtained from on-chip sensors. Particular emphasis is given to cost-efficient implementation reflected in: 1) low energy and area budgets of circuits dedicated to neural networks; 2) robust learning in presence of analog inaccuracies; and 3) long-term retention of learned functionality. Our chip consists of a reconfigurable array of synapses and neurons operating below threshold and featuring sub-μW power consumption. The synapse circuits employ dual-mode weight storage: 1) a dynamic mode, for fast bidirectional weight updates during training and 2) a nonvolatile mode, for permanent storage of learned functionality. We discuss a robust learning strategy, and we evaluate the system performance on several benchmark problems, such as the XOR2-6 and two-spirals classification tasks.
authors.forename: Dzmitry
authors.initials: D
authors.lastname: Maliuk
authors.forename: Yiorgos
authors.initials: Y
authors.lastname: Makris
mesh_terms: D032701:Biomimetics / Q000295:instrumentation*; D000069550:Machine Learning; D016571:Neural Networks, Computer; D009474:Neurons; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013569:Synapses