Neuromorphic Computing to make AI Energy-efficient: A Review of Contemporary Architectures, Difficulties and Applications
Keywords:
Brain-inspired hardware, Cognitive computing, Edge AI, Energy-efficient AI, Memristors, Neuromorphic computing, Spiking neural networksAbstract
Artificial intelligence is placing increasingly demanding requirements, especially in data-intensive and real-time applications, on the efficiency and scalability of traditional Von Neumann computing architectures, hence causing huge energy demands and computational bottlenecks. This article gives a detailed overview of neuromorphic computing, which is a relatively new paradigm that finds its inspiration in highly efficient systems and provides a potential solution to these limitations. The work defines the foundational principles of neuromorphic systems, such as the details of Spiking Neural Networks (SNNs), the benefits of event-driven processing, and the synaptic plasticity processes, which, together, will allow never-before-seen energy efficiency to be achieved. It gives a more detailed analysis of various hardware implementations that include digital and analog neuromorphic chips, etc. In addition, the review also discusses the wide range of applications in which neuromorphic systems outperform, such as real-time edge AI and autonomous robotics. The major achievements are the description of modern neuromorphic hardware and an overview of all existing and potential applications.
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