标题： TNNGen：用于时间序列聚类的神经形态感知处理单元的自动化设计 显示英文标题

标题： TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering

摘要： 时间神经网络（TNNs），一种特殊的脉冲神经网络，从新皮层中获得灵感，利用脉冲时间进行信息处理。 最近的研究提出了一个微架构框架和定制的宏单元套件，用于设计高能效的应用特定TNNs。 这些最新研究依赖于手动硬件设计，这是一个劳动密集且耗时的过程。 此外，目前还没有针对TNNs的开源功能仿真框架。 本文介绍了TNNGen，这是从PyTorch软件模型到后布局网表自动设计TNNs的开创性努力。 TNNGen包括一个新颖的PyTorch功能模拟器（用于TNN建模和应用探索）以及一个基于Python的硬件生成器（用于PyTorch到RTL和RTL到布局的转换）。 对跨多种感觉模态的时间序列信号聚类的七种代表性TNN设计进行了仿真，并评估了其后布局硬件复杂性和设计运行时间，以证明TNNGen的有效性。 我们还强调了TNNGen在不运行硬件工艺流程的情况下准确预测硅片指标的能力。 显示英文摘要

摘要： Temporal Neural Networks (TNNs), a special class of spiking neural networks, draw inspiration from the neocortex in utilizing spike-timings for information processing. Recent works proposed a microarchitecture framework and custom macro suite for designing highly energy-efficient application-specific TNNs. These recent works rely on manual hardware design, a labor-intensive and time-consuming process. Further, there is no open-source functional simulation framework for TNNs. This paper introduces TNNGen, a pioneering effort towards the automated design of TNNs from PyTorch software models to post-layout netlists. TNNGen comprises a novel PyTorch functional simulator (for TNN modeling and application exploration) coupled with a Python-based hardware generator (for PyTorch-to-RTL and RTL-to-Layout conversions). Seven representative TNN designs for time-series signal clustering across diverse sensory modalities are simulated and their post-layout hardware complexity and design runtimes are assessed to demonstrate the effectiveness of TNNGen. We also highlight TNNGen's ability to accurately forecast silicon metrics without running hardware process flow.