Spike-based Neuromorphic Computing for Next-Generation Computer Vision
This is an incremental contribution, as it reviews existing neuromorphic approaches without introducing new methods or data.
The chapter explores spike-based neuromorphic computing as an energy-efficient alternative to deep convolutional neural networks for computer vision, addressing the exponential growth in energy consumption from larger datasets and resource-constrained edge devices, but does not present specific experimental results or concrete numbers.
Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating brain functionality which can be realized through innovation in different abstraction layers including material, device, circuit, architecture and algorithm. As the energy consumption in complex vision tasks keep increasing exponentially due to larger data set and resource-constrained edge devices become increasingly ubiquitous, spike-based neuromorphic computing approaches can be viable alternative to deep convolutional neural network that is dominating the vision field today. In this book chapter, we introduce neuromorphic computing, outline a few representative examples from different layers of the design stack (devices, circuits and algorithms) and conclude with a few exciting applications and future research directions that seem promising for computer vision in the near future.