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

arXiv: https://arxiv.org/abs/2412.17977 · HTML: https://mlsanity.com/paper/2412.17977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [A Temporal Neural Network Architecture for Online Learning](https://mlsanity.com/paper/2011.13844/) (2020)
- [A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks](https://mlsanity.com/paper/2105.13262/) (2021)
- [Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks](https://mlsanity.com/paper/2102.09200/) (2021)
- [TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs](https://mlsanity.com/paper/2205.07410/) (2022)
- [Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units](https://mlsanity.com/paper/2205.14248/) (2022)

## Abstract (arXiv)

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.