DynapSIM: A Fast, Optimizable, and Mismatch Aware Mixed-Signal Neuromorphic Chip Simulator

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2022-8-22

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Çakal, Uğurcan

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Mixed-signal neuromorphic processors utilize analog signal processing and digital asynchronous communication, inspired by biological nervous systems' operation principles. Although these architectures provide an enormous power efficiency advantage over existing neural network inference systems, the difficulties in the configuration are one of the most fundamental obstacles in front of developing applications. Limited controllability over the analog hardware parameters, unintended variations inherent to a device's hardware makeup, and linearly inseparable bias space makes this hard to deliver an application that works as intended. It usually requires months of manual calibration effort of highly qualified researchers. Filling the gap, this study presents a software toolchain that allows an offline optimization of a hardware configuration that reflects a spiking neural network implementation. The results show how an abstract spiking neural network accurately and reliably translates into VLSI neuron and synapse configuration in a noisy environment. Proposed methods can be tailored to any mixed-signal neuromorphic processor architecture.

Subject Keywords

Mixed-Signal, Chip Simulation, Dynap-SE1, Dynap-SE2, Neuromorphic Computing, Neuromorphic Hardware, Non-Von-Neumann Computing, Silicon Brain, Spiking Neural Networks

URI

https://hdl.handle.net/11511/98616

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

U. Çakal, “DynapSIM: A Fast, Optimizable, and Mismatch Aware Mixed-Signal Neuromorphic Chip Simulator,” M.S. - Master of Science, Middle East Technical University, 2022.