Neuromorphic systems using in-memory or event-driven computing are motivated by the need for more energy-efficient processing of artificial intelligence workloads. Emerging neuromorphic architectures aim to combine traditional digital designs with the computational efficiency of analog computing and novel device technologies. A crucial problem in the rapid exploration and co-design of such architectures is the lack of tools for fast and accurate modeling and simulation. Typical mixed-signal design tools integrate a digital simulator with an analog solver like SPICE, which is prohibitively slow for large systems. By contrast, behavioral modeling of analog components is faster, but existing approaches are fixed to specific architectures with limited energy and performance modeling. In this paper, we propose LASANA, a novel approach that leverages machine learning to derive data-driven surrogate models of analog sub-blocks in a digital backend architecture. LASANA uses SPICE-level simulations of a circuit to train ML models that predict circuit energy, performance, and behavior at analog/digital interfaces. Such models can provide energy and performance annotation on top of existing behavioral models or function as replacements to analog simulation. We apply LASANA to an analog crossbar array and a spiking neuron circuit. Running MNIST and spiking MNIST, LASANA surrogates demonstrate up to three orders of magnitude speedup over SPICE, with energy, latency, and behavioral error less than 7%, 8%, and 2%, respectively.

翻译：采用内存计算或事件驱动计算的神经形态系统，其发展动力源于对人工智能工作负载进行更节能处理的需求。新兴的神经形态架构旨在将传统数字设计与模拟计算的计算效率及新型器件技术相结合。在此类架构的快速探索与协同设计中，一个关键问题在于缺乏能够进行快速精确建模与仿真的工具。典型的混合信号设计工具将数字仿真器与SPICE等模拟求解器集成，这对于大型系统而言速度极慢。相比之下，模拟组件的行为建模速度更快，但现有方法局限于特定架构，且能量和性能建模能力有限。本文提出LASANA，一种利用机器学习在数字后端架构中推导模拟子模块数据驱动代理模型的新方法。LASANA利用电路级SPICE仿真训练机器学习模型，以预测电路在模拟/数字接口处的能量、性能和行为。此类模型可在现有行为模型基础上提供能量和性能标注，或作为模拟仿真的替代方案。我们将LASANA应用于模拟交叉阵列和脉冲神经元电路。在运行MNIST和脉冲MNIST任务时，LASANA代理模型相比SPICE实现了高达三个数量级的加速，其能量、延迟和行为误差分别低于7%、8%和2%。