# Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor

arXiv: https://arxiv.org/abs/2307.13762 · HTML: https://mlsanity.com/paper/2307.13762/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions](https://mlsanity.com/paper/2002.03553/) (2020)
- [Replicating Kernels with a Short Stride Allows Sparse Reconstructions with Fewer Independent Kernels](https://mlsanity.com/paper/1406.4205/) (2014)
- [Image Compression: Sparse Coding vs. Bottleneck Autoencoders](https://mlsanity.com/paper/1710.09926/) (2017)
- [Dictionary Learning with Accumulator Neurons](https://mlsanity.com/paper/2205.15386/) (2022)
- [Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks](https://mlsanity.com/paper/1710.09875/) (2017)

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

- [WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm](https://mlsanity.com/paper/2410.18794/) (2024)
- [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)
- [Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure](https://mlsanity.com/paper/2403.02236/) (2024)
- [Convolutional Sparse Coding via the Locally Competitive Algorithm on Loihi 2](https://mlsanity.com/paper/2606.08584/) (2026)

## Abstract (arXiv)

Neuromorphic processors have garnered considerable interest in recent years for their potential in energy-efficient and high-speed computing. The Locally Competitive Algorithm (LCA) has been utilized for power efficient sparse coding on neuromorphic processors, including the first Loihi processor. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is orders of magnitude more efficient and faster for large sparsity penalties, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent, autonomous, real-time processing on small robots, satellites where there are strict SWaP (small, lightweight, and low power) requirements. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices.