# Efficient Video and Audio processing with Loihi 2

arXiv: https://arxiv.org/abs/2310.03251 · HTML: https://mlsanity.com/paper/2310.03251/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2023.

## Built on by (most cited first, 29 of 29)

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- [Solving Sparse Finite Element Problems on Neuromorphic Hardware](https://mlsanity.com/paper/2501.10526/) (2025)
- [Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2](https://mlsanity.com/paper/2503.18002/) (2025)
- [An efficient probabilistic hardware architecture for diffusion-like models](https://mlsanity.com/paper/2510.23972/) (2025)
- [A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing](https://mlsanity.com/paper/2409.15022/) (2024)
- [Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review](https://mlsanity.com/paper/2603.08725/) (2026)
- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity](https://mlsanity.com/paper/2502.01330/) (2025)
- [FeNN: A RISC-V vector processor for Spiking Neural Network acceleration](https://mlsanity.com/paper/2506.11760/) (2025)
- [Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling](https://mlsanity.com/paper/2509.17186/) (2025)
- [A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2](https://mlsanity.com/paper/2510.13757/) (2025)
- [N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2](https://mlsanity.com/paper/2408.13379/) (2024)
- [Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization](https://mlsanity.com/paper/2409.15849/) (2024)
- [Region Masking to Accelerate Video Processing on Neuromorphic Hardware](https://mlsanity.com/paper/2503.16775/) (2025)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [CATFormer: When Continual Learning Meets Spiking Transformers With Dynamic Thresholds](https://mlsanity.com/paper/2603.15184/) (2026)
- [Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems](https://mlsanity.com/paper/2412.05126/) (2024)
- [Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons](https://mlsanity.com/paper/2506.20015/) (2025)
- [Event-driven Robust Fitting on Neuromorphic Hardware](https://mlsanity.com/paper/2508.09466/) (2025)
- [ASecond-Order SpikingSSM for Wearables](https://mlsanity.com/paper/2510.14386/) (2025)
- [Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network](https://mlsanity.com/paper/2511.01553/) (2025)
- [Realizing Fully-Integrated, Low-Power, Event-Based Pupil Tracking with Neuromorphic Hardware](https://mlsanity.com/paper/2511.20175/) (2025)
- [Autonomous Reinforcement Learning Robot Control with Intel's Loihi 2 Neuromorphic Hardware](https://mlsanity.com/paper/2512.03911/) (2025)
- [DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data](https://mlsanity.com/paper/2603.09274/) (2026)
- [The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing](https://mlsanity.com/paper/2607.24396/) (2026)
- [Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring](https://mlsanity.com/paper/2608.18341/) (2026)

## Abstract (arXiv)

Loihi 2 is an asynchronous, brain-inspired research processor that generalizes several fundamental elements of neuromorphic architecture, such as stateful neuron models communicating with event-driven spikes, in order to address limitations of the first generation Loihi. Here we explore and characterize some of these generalizations, such as sigma-delta encapsulation, resonate-and-fire neurons, and integer-valued spikes, as applied to standard video, audio, and signal processing tasks. We find that these new neuromorphic approaches can provide orders of magnitude gains in combined efficiency and latency (energy-delay-product) for feed-forward and convolutional neural networks applied to video, audio denoising, and spectral transforms compared to state-of-the-art solutions.