TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
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arXiv
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| Format: | Preprint |
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2024
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| author | Vellaisamy, Prabhu Nair, Harideep Ratnakaram, Vamsikrishna Gupta, Dhruv Shen, John Paul |
| author_facet | Vellaisamy, Prabhu Nair, Harideep Ratnakaram, Vamsikrishna Gupta, Dhruv Shen, John Paul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Vellaisamy, Prabhu Nair, Harideep Ratnakaram, Vamsikrishna Gupta, Dhruv Shen, John Paul Hardware Architecture Artificial Intelligence Neural and Evolutionary Computing 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. |
| title | TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering |
| topic | Hardware Architecture Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2412.17977 |