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.

翻译：Loihi 2是一款异步、受大脑启发的研究处理器，其泛化了神经形态架构的若干基本要素（例如通过事件驱动脉冲通信的带状态神经元模型），以解决第一代Loihi的局限性。本文探讨并表征了这些泛化特性在标准视频、音频及信号处理任务中的应用，包括sigma-delta封装、共振-放电神经元以及整数值脉冲。研究发现，相较于现有最优解决方案，这些新型神经形态方法在应用于前馈与卷积神经网络的视频、音频降噪及频谱变换任务时，能够在效率与延迟（能量-延迟积）方面实现数量级的综合提升。