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Neuromorphic computing and, in particular, spiking neural networks (SNNs)have become an attractive alternative to deep neural networks for a broad rangeof signal processing applications, processing static and/or temporal inputsfrom different sensory modalities, including audio and vision sensors. In thispaper, we start with a description of recent advances in algorithmic andoptimization innovations to efficiently train and scale low-latency, andenergy-efficient spiking neural networks (SNNs) for complex machine learningapplications. We then discuss the recent efforts in algorithm-architectureco-design that explores the inherent trade-offs between achieving highenergy-efficiency and low latency while still providing high accuracy andtrustworthiness. We then describe the underlying hardware that has beendeveloped to leverage such algorithmic innovations in an efficient way. Inparticular, we describe a hybrid method to integrate significant portions ofthe model's computation within both memory components as well as the sensoritself. Finally, we discuss the potential path forward for research in buildingdeployable SNN systems identifying key challenges in thealgorithm-hardware-application co-design space with an emphasis ontrustworthiness.

Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology