TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
| Autor: | Vellaisamy, Prabhu, Nair, Harideep, Ratnakaram, Vamsikrishna, Gupta, Dhruv, Shen, John Paul |
|---|---|
| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/TCSII.2024.3390002 |
| Popis: | 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. Comment: Published in IEEE Transactions on Circuits and Systems II: Express Briefs, May 2024 |
| Databáze: | arXiv |
| Externí odkaz: |
|