# Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation Sparsification

arXiv: https://arxiv.org/abs/2407.20421 · HTML: https://mlsanity.com/paper/2407.20421/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 24 of 24)

- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks](https://mlsanity.com/paper/1708.04485/) (2017)
- [The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers](https://mlsanity.com/paper/2210.06313/) (2022)
- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks](https://mlsanity.com/paper/2003.06696/) (2020)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [Accelerating Convolutional Neural Networks via Activation Map Compression](https://mlsanity.com/paper/1812.04056/) (2018)
- [Optical flow estimation from event-based cameras and spiking neural networks](https://mlsanity.com/paper/2302.06492/) (2023)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics](https://mlsanity.com/paper/2209.11741/) (2022)
- [Event-based Temporally Dense Optical Flow Estimation with Sequential Learning](https://mlsanity.com/paper/2210.01244/) (2022)
- [DeFiNES: Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators through Analytical Modeling](https://mlsanity.com/paper/2212.05344/) (2022)
- [Neuromorphic Optical Flow and Real-time Implementation with Event Cameras](https://mlsanity.com/paper/2304.07139/) (2023)
- [Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity](https://mlsanity.com/paper/2309.06626/) (2023)
- [Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design](https://mlsanity.com/paper/2303.15224/) (2023)

## Built on by (most cited first, 7 of 7)

- [Learning Normal Flow Directly From Event Neighborhoods](https://mlsanity.com/paper/2412.11284/) (2024)
- [Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection](https://mlsanity.com/paper/2506.13440/) (2025)
- [Context-aware Sparse Spatiotemporal Learning for Event-based Vision](https://mlsanity.com/paper/2508.19806/) (2025)
- [ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2503.10195/) (2025)
- [A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks](https://mlsanity.com/paper/2510.20683/) (2025)
- [Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition](https://mlsanity.com/paper/2606.01135/) (2026)
- [Towards an Expressivity-Normalized Energy-Demand Comparison of ANNs and SNNs](https://mlsanity.com/paper/2608.29869/) (2026)

## Abstract (arXiv)

Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair comparison is missing in the literature. In this work, we propose an event-based optical flow solution based on activation sparsification and a neuromorphic processor, SENECA. SENECA has an event-driven processing mechanism that can exploit the sparsity in ANN activations and SNN spikes to accelerate the inference of both types of neural networks. The ANN and the SNN for comparison have similar low activation/spike density (~5%) thanks to our novel sparsification-aware training. In the hardware-in-loop experiments designed to deduce the average time and energy consumption, the SNN consumes 44.9ms and 927.0 microjoules, which are 62.5% and 75.2% of the ANN's consumption, respectively. We find that SNN's higher efficiency attributes to its lower pixel-wise spike density (43.5% vs. 66.5%) that requires fewer memory access operations for neuron states.