  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.
