论文标题
Neucasl：从逻辑设计到神经形成引擎的系统模拟
NeuCASL: From Logic Design to System Simulation of Neuromorphic Engines
论文作者
论文摘要
随着摩尔定律饱和和丹纳德的缩放率撞到了墙壁，传统的冯·诺伊曼系统无法为CNN等计算密集型算法提供GFLOPS/WATT。非常规计算方法的最新趋势使我们希望为此类算法设计高能节能的计算系统。神经形态计算是一种有希望的方法，其脑启发的电路，新兴技术的使用和低功率性质。研究人员使用各种新型技术，例如回忆录，硅光子学，鳍片和碳纳米管来演示神经形态计算机。但是，从神经形态逻辑设计开始并进行建筑模拟的灵活CAD工具尚未得到证明，以支持这种有希望的范式的兴起。在这个项目中，我们旨在构建Neucasl，这是一个基于Opensource Python的完整系统CAD框架，用于神经形态逻辑设计，电路模拟以及系统性能和可靠性估算。据我们所知，这是同类产品中的第一个。
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.
