# A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks

arXiv: https://arxiv.org/abs/2105.13262 · HTML: https://mlsanity.com/paper/2105.13262/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 11 of 11)

- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Training Deep Spiking Neural Networks using Backpropagation](https://mlsanity.com/paper/1608.08782/) (2016)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [Supervised learning based on temporal coding in spiking neural networks](https://mlsanity.com/paper/1606.08165/) (2016)
- [Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks](https://mlsanity.com/paper/1804.00227/) (2018)
- [Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition](https://mlsanity.com/paper/1504.03871/) (2015)
- [A Temporal Neural Network Architecture for Online Learning](https://mlsanity.com/paper/2011.13844/) (2020)
- [Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks](https://mlsanity.com/paper/2102.09200/) (2021)
- [(Newtonian) Space-Time Algebra](https://mlsanity.com/paper/2001.04242/) (2019)
- [A Neuromorphic Paradigm for Online Unsupervised Clustering](https://mlsanity.com/paper/2005.04170/) (2020)

## Built on by (most cited first, 4 of 4)

- [tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI](https://mlsanity.com/paper/2412.17966/) (2024)
- [TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs](https://mlsanity.com/paper/2205.07410/) (2022)
- [C3S Micro-architectural Enhancement: Spike Encoder Block and Relaxing Gamma Clock (Asynchronous)](https://mlsanity.com/paper/2306.15093/) (2023)
- [TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering](https://mlsanity.com/paper/2412.17977/) (2024)

## Abstract (arXiv)

Temporal Neural Networks (TNNs) are spiking neural networks that use time as a resource to represent and process information, similar to the mammalian neocortex. In contrast to compute-intensive deep neural networks that employ separate training and inference phases, TNNs are capable of extremely efficient online incremental/continual learning and are excellent candidates for building edge-native sensory processing units. This work proposes a microarchitecture framework for implementing TNNs using standard CMOS. Gate-level implementations of three key building blocks are presented: 1) multi-synapse neurons, 2) multi-neuron columns, and 3) unsupervised and supervised online learning algorithms based on Spike Timing Dependent Plasticity (STDP). The proposed microarchitecture is embodied in a set of characteristic scaling equations for assessing the gate count, area, delay and power for any TNN design. Post-synthesis results (in 45nm CMOS) for the proposed designs are presented, and their online incremental learning capability is demonstrated.