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With Moore's law saturating and Dennard scaling hitting its wall, traditionalVon Neuman systems cannot offer the GFlops/watt for compute-intensivealgorithms such as CNN. Recent trends in unconventional computing approachesgive us hope to design highly energy-efficient computing systems for suchalgorithms. Neuromorphic computing is a promising such approach with itsbrain-inspired circuitry, use of emerging technologies, and low-power nature.Researchers use a variety of novel technologies such as memristors, siliconphotonics, FinFET, and carbon nanotubes to demonstrate a neuromorphic computer.However, a flexible CAD tool to start from neuromorphic logic design and go upto architectural simulation is yet to be demonstrated to support the rise ofthis promising paradigm. In this project, we aim to build NeuCASL, anopensource python-based full system CAD framework for neuromorphic logicdesign, circuit simulation, and system performance and reliability estimation.This is a first of its kind to the best of our knowledge.

NeuCASL: From Logic Design to System Simulation of Neuromorphic Engines