Exploring Neuromorphic Computing with Deep Learning
Yogesh Kumar Sharma, Smitha, Shaik Saddam Hussain, Leena Arya · 2025
To create more intelligent systems that use less energy, researchers are looking at how deep learning and neuromorphic computing might work together. Although deep learning algorithms have shown to be very effective in several domains, the prohibitive computing costs linked to training them have limited their use. Neuromorphic computing, a new method that takes motivation from the biology and structure of the brain of an individual, offers promise by using practical artificial brain cells to do computations. By bringing together deep learning with intelligent devices that prioritize energy saving in their autonomous operations, this junction has the potential to pave the way for a genuinely ubiquitous AI. Neuromorphic hardware has several benefits over traditional digital computer designs, such as enormous data throughput, quicker processing speeds, reduced power consumption, more integration density, and analogue computing. This is why Neuromorphic hardware is a promising substitute for using deep learning models in practical settings. Reviewing neuromorphic computing using deep learning methodologies, this article discusses its potential, uses, and obstacles. We go over some of the potential benefits of neuromorphic computing technologies for the future of computing and the ways in which algorithms and apps built on these platforms might be improved.