Spiking neural networks (SNNs) mimic biological nervous systems. Using event-driven computation and communication, SNNs achieve very low power consumption. However, two important issues have persisted. First, directly training SNNs has not yielded competitive inference accuracy. Second, non-spike inputs must be converted to spike trains, resulting in long latency. Recently, SNN algorithm accuracy has improved significantly, aided by new training techniques, and commercial event-based dynamic vision sensors (DVSs) have emerged. Integrating a spike-based DVS with an SNN accelerator is a promising approach for end-to-end, event-driven operations. We also need accurate and hardware-aware SNN algorithms that can be directly trained with input spikes from a DVS while reducing storage and compute requirements. In this talk, we’ll introduce the characteristics, opportunities and challenges of SNNs, and present results from projects utilizing neuromorphic algorithms and custom hardware.