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

• URL: http://arxiv.org/abs/2412.17977v1
• Date: Mon, 23 Dec 2024 20:46:53 GMT
• Title: TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
• Authors: Prabhu Vellaisamy, Harideep Nair, Vamsikrishna Ratnakaram, Dhruv Gupta, John Paul Shen,
• Abstract summary: Recent works proposed a microarchitecture framework and custom macro suite for highly energy-efficient application-specific TNNs. There is no open-source functional simulation framework for TNNs. This paper introduces TNNGen, a pioneering effort towards automated design of TNNs from PyTorch software models to post-netlists.
• Score: 2.1041384320978267
• License:
• Abstract: 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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