发表机构
KTH Royal Institute of Technology（KTH皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出连接计算神经科学建模与传统EDA流程的eNDA视角，开发Syn2Logic框架，无需编写HDL即可生成性能优异的加速器与求解器，在秀丽隐杆线虫、数独、MNIST任务上均优于现有方案。
AI 中文摘要
在本研究中，我们提出了电子神经形态设计自动化（eNDA）的视角，将其视为连接计算神经科学建模与传统电子设计自动化（EDA）流程的设计自动化流程。我们引入该术语，给出其实现示例，并设计了原型实现Syn2Logic。Syn2Logic是完整的eNDA框架，允许神经科学家使用自定义DSL对神经行为建模，编译器可将同一模型描述转换为可综合的RTL硬件。我们在论文末尾通过Syn2Logic应用eNDA流程，展示无需编写一行硬件描述语言（HDL）代码即可实现：（i）生成我们认为是最快的秀丽隐杆线虫（C. elegans）加速器，其运行速度显著优于最先进的模拟器；（ii）创建据我们所知最快、最通用的神经形态数独求解器，在TOP1465谜题上的性能优于CP-SAT和SCIP；（iii）在小型FPGA上创建5.6百万帧/瓦（FPS/Watt）的加速器，在MNIST数据集上的速度和能效优于现有神经形态架构。
英文摘要
In this work, we propose a view on electronic Neuromorphic Design Automation (eNDA), which we see as a design automation flow that bridges computational neuroscience modeling with traditional Electronic Design Automation (EDA) flow. We introduce the term, give examples of how it can be implemented, and design a prototype implementation: Syn2Logic. Syn2Logic is an entire eNDA framework, that allows neuroscientists to model neural behavior using a custom DSL and a compiler that takes the same model description down to synthesizable RTL hardware. We end the paper by applying the eNDA-flow through Syn2Logic to show how to -- without writing a single line of hardware description language (HDL) code-- (i) generate what we believe is the fastest C. elegans accelerator that runs significantly faster than state-of-the-art simulators, (ii) create (to the best of our knowledge) the fastest, most generic neuromorphic sudoku solver that outperforms CP-SAT and SCIP on TOP1465 puzzles, (iii) create a 5.6 million FPS/Watt accelerator on a tiny FPGA that outperforms existing neuromorphic architectures in terms of speed and energy-efficiency on the MNIST dataset, and (iv) create a generic accelerator for unsupervised learning that outperforms most existing unsupervised methods on the OPS-SAT benchmark.
