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Brain-Inspired Neuromorphic Computing
Reviewed by Dr Srikanth Ponnada
Brain-Inspired Neuromorphic Computing
Brain-Inspired Neuromorphic Computing: Transforming the Future of AI
With the ongoing advancement of artificial intelligence (AI), neuromorphic computing is emerging as an innovative method that replicates the neuronal architecture of the human brain. This new paradigm seeks to improve the efficiency and capacities of computer systems, potentially transforming sectors like robotics and healthcare. Neuromorphic systems emulate biological neurons in information processing by employing spiking neural networks (SNNs) for enhanced data efficiency. In contrast to conventional computer architectures that depend on binary operations, neuromorphic systems utilize spike-based signals to convey information in a far more energy-efficient way. This facilitates real-time processing and decision-making, essential for applications such as driverless cars and live video analysis.
Recent advancements in neuromorphic devices have considerably enhanced their speed and energy efficiency relative to traditional CPUs. These chips excel at intricate activities, like pattern recognition and sensory processing, while utilizing minimum energy. Consequently, they are poised to spearhead innovation in edge computing, where rapid, localized data processing is essential. Neuromorphic computing is being investigated in healthcare for applications like brain-computer interfaces and sophisticated diagnostic instruments. By closely replicating human cognition, these computers can analyze medical data with enhanced accuracy, resulting in early illness identification and tailored therapy alternatives. Nonetheless, the domain continues to encounter obstacles, such as the necessity for standardized programming frameworks and broader industry acceptance. Notwithstanding these challenges, further research may enable neuromorphic computing to initiate a novel phase of AI that enhances computational capabilities while more accurately reflecting the intricacies of human intellect. The onset of this technological revolution carries significant societal ramifications, potentially transforming our interactions with computers and maximizing the capabilities of AI.
Image: Istock
References & Suggested Reading
1. Mehonic, A., & Kenyon, A. J. (2022). Brain-inspired computing needs a master plan. Nature, 604(7905), 255–260. https://doi.org/10.1038/s41586-021-04362-w
2. Schuman, C. D., Kulkarni, S. R., Parsa, M., Mitchell, J. P., Date, P., & Kay, B. (2022). Opportunities for neuromorphic computing algorithms and applications. Nature Computational Science, 2(1), 10–19. https://doi.org/10.1038/s43588-021-00184-y
Authored by Dr Srikanth Ponnada
Dr Srikanth Ponnada, PhD, MRSC
-CEO, Editor & Senior Scientific Content Author
Dr. Ponnada, is a senior researcher at VSB-Technical University-Ostrava; he previously worked as a Post-Doctoral Fellow at Prof. Herring’s group, Chemical and Biological Engineering Department, Colorado School of Mines-U.S.A, as a Post-Doctoral Research Associate at Indian Institute of Technology Jodhpur-Rajasthan. His Ph.D. research focused on “Functional Materials and Their Electrochemical Applications in Batteries and Sensors.” His research area covers Functional Materials Synthesis, Polymer electrolyte membranes, Device fabrication, conversion devices (Fuel cells and Electrolyzers), Energy storage, Electrocatalysis, Electrochemical Sensors, Artificial Intelligence, and LLM (generative AI) in energy. He has also held research positions at CSIR-Central Electrochemical Research Institute, where he worked on lead-free perovskite-based photovoltaics and electrocatalysis, and at IIT (ISM) Dhanbad, where he contributed to research on gold nanoparticle-assisted heterogeneous catalysis and alcohol oxidation reactions. Also, he is an Early Career Member at the Electrochemical Society (ECS), a Member at AIChE and a Life Member at the Indian Carbon Society (ICS), also an astronomy and astrophotography enthusiast.
