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.

翻译：神经形态处理器近年来因其在能效与高速计算方面的潜力而备受关注。局部竞争算法（LCA）已被用于在神经形态处理器（包括第一代Loihi处理器）上实现高能效稀疏编码。随着Loihi 2处理器支持自定义神经元模型和分级脉冲通信，更复杂的LCA实现成为可能。我们提出了一种专为Loihi 2处理器设计的新型LCA实现，并通过初步基准测试将其与CPU和GPU设备上的LCA进行对比。实验表明，在较大稀疏惩罚条件下，Loihi 2上的LCA在保持相似重建质量的同时，能效和速度均提升数个数量级。我们发现，当LCA参数向更高表示稀疏性优化时，这一性能优势进一步扩大。本研究凸显了神经形态处理器（尤其是Loihi 2）在满足严格SWaP（小型、轻量、低功耗）要求的小型机器人、卫星等设备上实现智能、自主、实时处理的潜力。通过证明Loihi 2上LCA相比传统计算设备的优越性能，本研究提示Loihi 2或将成为推动此类应用发展的关键工具。总体而言，我们的研究揭示了神经形态处理器在资源受限设备上实现高效精准数据处理的巨大潜力。