# STEMS: Spatial-Temporal Mapping For Spiking Neural Networks

arXiv: https://arxiv.org/abs/2502.03287 · HTML: https://mlsanity.com/paper/2502.03287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [Spike-driven Transformer](https://mlsanity.com/paper/2307.01694/) (2023)
- [Deep Directly-Trained Spiking Neural Networks for Object Detection](https://mlsanity.com/paper/2307.11411/) (2023)
- [NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems](https://mlsanity.com/paper/2304.04640/) (2023)
- [Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review](https://mlsanity.com/paper/2202.08897/) (2022)
- [Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling](https://mlsanity.com/paper/2205.05826/) (2022)
- [Origami: A 803 GOp/s/W Convolutional Network Accelerator](https://mlsanity.com/paper/1512.04295/) (2015)
- [Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators](https://mlsanity.com/paper/2212.10612/) (2022)
- [Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/1908.08024/) (2019)
- [A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware](https://mlsanity.com/paper/2108.12444/) (2021)
- [Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2103.05707/) (2021)
- [Real-Time Scheduling of Machine Learning Operations on Heterogeneous Neuromorphic SoC](https://mlsanity.com/paper/2209.14777/) (2022)
- [SNEAP: A Fast and Efficient Toolchain for Mapping Large-Scale Spiking Neural Network onto NoC-based Neuromorphic Platform](https://mlsanity.com/paper/2004.01639/) (2020)
- [Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2010.04773/) (2020)
- [THOR -- A Neuromorphic Processor with 7.29G TSOP2/mm2Js Energy-Throughput Efficiency](https://mlsanity.com/paper/2212.01696/) (2022)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) are promising bio-inspired third-generation neural networks. Recent research has trained deep SNN models with accuracy on par with Artificial Neural Networks (ANNs). Although the event-driven and sparse nature of SNNs show potential for more energy efficient computation than ANNs, SNN neurons have internal states which evolve over time. Keeping track of SNN states can significantly increase data movement and storage requirements, potentially losing its advantages with respect to ANNs. This paper investigates the energy effects of having neuron states, and how it is influenced by the chosen mapping to realistic hardware architectures with advanced memory hierarchies. Therefore, we develop STEMS, a mapping design space exploration for SNNs. STEMS models SNN's stateful behavior and explores intra-layer and inter-layer mapping optimizations to minimize data movement, considering both spatial and temporal SNN dimensions. Using STEMS, we show up to 12x reduction in off-chip data movement and 5x reduction in energy (on top of intra-layer optimizations), on two event-based vision SNN benchmarks. Finally, neuron states may not be needed for all SNN layers. By optimizing neuron states for one of our benchmarks, we show 20x reduction in neuron states and 1.4x better performance without accuracy loss.