Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications, processing static and/or temporal inputs from different sensory modalities, including audio and vision sensors. In this paper, we start with a description of recent advances in algorithmic and optimization innovations to efficiently train and scale low-latency, and energy-efficient spiking neural networks (SNNs) for complex machine learning applications. We then discuss the recent efforts in algorithm-architecture co-design that explores the inherent trade-offs between achieving high energy-efficiency and low latency while still providing high accuracy and trustworthiness. We then describe the underlying hardware that has been developed to leverage such algorithmic innovations in an efficient way. In particular, we describe a hybrid method to integrate significant portions of the model's computation within both memory components as well as the sensor itself. Finally, we discuss the potential path forward for research in building deployable SNN systems identifying key challenges in the algorithm-hardware-application co-design space with an emphasis on trustworthiness.

翻译：神经形态计算，特别是脉冲神经网络（SNNs），已成为深度神经网络在广泛信号处理应用中的有吸引力的替代方案，能够处理来自不同感知模态（包括音频和视觉传感器）的静态和/或时序输入。本文首先描述了算法与优化创新的最新进展，以高效训练和扩展低延迟、高能效的脉冲神经网络（SNNs），用于复杂的机器学习应用。随后，我们讨论了近期算法-架构协同设计方面的努力，该设计探索了实现高能效与低延迟同时保持高精度和可信性之间的固有权衡。接着，我们描述了为高效利用此类算法创新而开发的底层硬件。特别地，我们介绍了一种混合方法，将模型计算的显著部分集成到存储组件以及传感器本身中。最后，我们探讨了构建可部署SNN系统的潜在研究路径，识别了算法-硬件-应用协同设计空间中的关键挑战，并重点强调可信性。