自动化设计神经形态时序聚类芯片，提升能效与开发效率
TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
- 从PyTorch模型自动生成后布局网表，打通软硬件协同设计流程
- 7种跨模态时序信号聚类设计验证，平均设计耗时降低至1.5小时
- 无需实际流片即可精准预测芯片面积与功耗，适合边缘计算硬件研发者
时间神经网络（TNN）是一类受新皮层启发的脉冲神经网络，利用脉冲时间进行信息处理。近期工作提出了专用TNN的微架构框架与定制宏集，实现高能效应用。然而，现有方法依赖人工硬件设计，过程繁琐耗时，且缺乏开源功能仿真框架。本文提出TNNGen，首个从PyTorch软件模型到后布局网表的TNN自动化设计工具。TNNGen包含新型PyTorch功能仿真器（用于TNN建模与应用探索）和基于Python的硬件生成器（完成PyTorch到RTL、RTL到布局的转换）。针对多种感官模态的时间序列信号聚类任务，对7个代表性TNN设计进行了仿真，并评估其后布局硬件复杂度与设计运行时间，验证了该工具的有效性。此外，还展示了其无需执行硬件工艺流程即可准确预测硅片指标的能力。
原文摘要 · Abstract (English)
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.
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