Artificial Intelligence (AI) has enabled a plethora of applications today, ranging from the most recent chatbots that give you a human-like question/answer experience to autonomous driving cars. But, all these massive feats with AI incur huge costs in terms of energy, memory, and power consumption. In the past decade, Spiking Neural Networks (SNNs) have emerged as a low-power alternative to AI. SNN’s main attraction lies in the fact that they offer low-power architectural implementations, especially for arithmetic operations. Furthermore, unlike traditional neural networks, SNNs process information over time and the temporal dimension, if leveraged suitably, can help enable the next generation of AI applications at lower cost with better performance and robustness. However, training SNNs suitably for realistic tasks has been a long-standing challenge. This project innovates on fundamental optimization strategies, using the temporal features in SNNs to yield new architectures with diverse connectivity and sparsity that yield significant energy-efficiency benefits for distributed low-power edge computing applications. Furthermore, this research will support the interdisciplinary development of Ph.D. and undergraduate students and provides a unique education infrastructure to train the next generation of electrical and computer engineering researchers and practitioners. Today, deploying large-scale spiking neural networks (SNNs) for realistic computer vision and related tasks