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

使用 python 构建了一个开源的适用于神经形态逻辑设计、电路模拟、系统性能和可靠性评估的完整系统 CAD 框架 NeuCASL。