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.
翻译:随着摩尔法律的饱和和和登纳德的伸缩,传统的Von Neuman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近趋势使我们有希望为这种算法设计高能效的计算系统。 Neurormophic 计算是一种充满希望的方法,它具有大脑启发的电路、新兴技术的使用和低功率性质。研究人员使用各种新技术,如记忆器、硅光子、FinFET和碳纳米管来演示神经形态计算机。然而,一个从神经形态逻辑设计开始并进入建筑模拟的灵活 CAD 工具还有待展示,以支持这一有希望的范式的兴起。 在这个项目中,我们的目标是为神经形态逻辑设计、电路模拟、系统性能和可靠性估计建立开放源的全系统CAD框架Neu CASL,这是一个开放源的全系统框架。这是我们最了解的首例。