Reviews And Perspectives In Neuromorphic Engineering Novel Neuromorphic Computing Approaches
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Reviews and Perspectives in Neuromorphic Engineering: Novel Neuromorphic Computing Approaches
Author: Pier Luigi Gentili
language: en
Publisher: Frontiers Media SA
Release Date: 2024-11-15
Frontiers in Neuroscience, Neuromorphic Engineering is delighted to present the ‘Reviews and Perspectives in’ series of article collections. Reviews and Perspectives in Neuromorphic Engineering: Novel Neuromorphic Computing Approaches Research Topic will publish high-quality scholarly reviews and perspective papers on key topics in Neuromorphic Computing. It aims to highlight recent advances in neuromorphic computing in software, hardware, and wetware whilst emphasizing important directions, novel and unconventional approaches, and new possibilities for future inquiries. The research presented will promote discussion in the neuromorphic computing community that will translate to best practice applications. We welcome Review, Mini Review, Opinion, General Commentary, and Perspective articles on themes including, but not limited to: • Innovative architectures and models in neuromorphic computing • Oscillatory Neural Networks computing • Reservoir computing • Chemical computing • Protein computing • Synthetic cells • Analog computing • Bayesian inference and fuzzy logic • Linking neuromorphic and quantum computing • Novel materials for neuromorphic computing • Unconventional neuromorphic approaches • Photonic computing • 3D integrated Neural Network • Physical Chemistry of materials and systems for neuromorphic computing.
Neuromorphic Engineering
The brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness traveling in cyberspace. However, studying the brain's core computation architecture can inspire scientists, computer architects, and algorithm designers to think fundamentally differently about their craft. Neuromorphic engineers have the ultimate goal of realizing machines with some aspects of cognitive intelligence. They aspire to design computing architectures that could surpass existing digital von Neumann-based computing architectures' performance. In that sense, brain research bears the promise of a new computing paradigm. As part of a complete cognitive hardware and software ecosystem, neuromorphic engineering opens new frontiers for neuro-robotics, artificial intelligence, and supercomputing applications. This book will present neuromorphic engineering from three perspectives: the scientist, the computer architect, and the algorithm designer. We will zoom in and out of the different disciplines, allowing readers with diverse backgrounds to understand and appreciate the field. Overall, the book will cover the basics of neuronal modeling, neuromorphic circuits, neural architectures, event-based communication, and the neural engineering framework. Readers will have the opportunity to understand the different views over the inherently multidisciplinary field of neuromorphic engineering.
Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.