IJBTT

Revolutionizing AI and Computing the Neuromorphic Engineering Paradigm in Neuromorphic Chips
	© 2024 by IJCTT Journal
	Volume-72 Issue-1
	Year of Publication : 2024
	Authors : Narayan Hampiholi
	DOI :  10.14445/22312803/IJCTT-V72I1P115

How to Cite?

Narayan Hampiholi, "Revolutionizing AI and Computing the Neuromorphic Engineering Paradigm in Neuromorphic Chips," International Journal of Computer Trends and Technology, vol. 72, no. 1, pp. 92-98, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I1P115

Abstract
This research explores the cutting-edge field of neuromorphic engineering, providing a thorough analysis of its principles, hardware design, and practical uses. It highlights that event-driven mechanisms, parallel processing, and synaptic plasticity are essential for neuromorphic chip design. This article examines the revolutionary influence of neuromorphic devices across multiple disciplines, such as speech recognition, robotics, and computer vision. Technical and ethical challenges are explained, emphasizing standardization, scalability, and societal ramifications. Besides, this research considers how neuromorphic chips can transform computers and artificial intelligence. It emphasizes the necessity of continual multidisciplinary research and innovation to overcome obstacles and realize this paradigm shift's full potential.
This research aims to define neuromorphic engineering and explain its goal to emulate the neural structure of the human brain to improve computational speed and efficiency. Provide insight into how the human brain processes information through a vast network of neurons and synapses and how this biological model inspires the architecture of neuromorphic chips. Explain how neuromorphic chips can potentially address the limitations of current AI technologies by enabling more efficient processing of complex algorithms and enhancing machine learning capabilities

Keywords
ML, AI, Robotics, Neuromorphic, Engineering, Computing, Devices, Sensor Networks, Chips, ENIAC.

Reference

[1] Chander Prakash et al., “Computing of Neuromorphic Materials: an Emerging Approach for Bioengineering Solutions,” Materials Advances, vol. 4, no. 23, pp. 5882-5919, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Catherine D. Schuman et al., “Opportunities for Neuromorphic Computing Algorithms and Applications,” Nature Computational Science, vol. 2, no. 1, pp. 10-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jia-Qin Yang et al., “Neuromorphic Engineering: From Biological to Spike‐Based Hardware Nervous Systems,” Advanced Materials, vol. 32, no. 52, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Von Neumann Architecture, Computer Science GCSE GURU, 2019. [Online]. Available: https://www.computerscience.gcse.guru/theory/von-neumann-architecture
[5] Kashu Yamazaki et al., “Spiking Neural Networks and Their Applications: A Review,” Brain Sciences, vol. 12, no. 7, pp. 1-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mahyar Shahsavari et al., “Advancements in Spiking Neural Network Communication and Synchronization Techniques for Event-Driven Neuromorphic Systems,” Array, vol. 20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Sacha Jennifer van Albada et al., “Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model,” Frontiers in Neuroscience, vol. 12, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Tanvir Ahmed, “Bio-Inspired Artificial Synapses: Neuromorphic Computing Chip Engineering with Soft Biomaterials,” Memories - Materials Devices Circuits and Systems, vol. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Filipp Akopyan et al., “TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 34, no. 10, pp. 1537-1557, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Short Overview of the Human Brain Project, Human Brain Project, 2023. [Online]. Available: https://www.humanbrainproject.eu/en/about/overview/
[11] A New Neuromorphic chip for AI on the Edge, at a Small Fraction of the Energy and Size of Today’s Compute Platforms, UCSan Diego, 2022. [Online]. Available: https://jacobsschool.ucsd.edu/news/release/3499
[12] Ruslan V. Kutluyarov et al., “Neuromorphic Photonics Circuits: Contemporary Review,” Nanomaterials, vol. 13, no. 24, pp. 1-36, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Min-Kyu Kim et al., “Emerging Materials for Neuromorphic Devices and Systems,” iScience, vol. 23, no. 12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Christian Pehle et al., “The BrainScaleS-2 Accelerated Neuromorphic System with Hybrid Plasticity,” Frontiers in Neuroscience, vol. 16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Pierre Bonzon, “Towards Neuro-Inspired Symbolic Models of Cognition: Linking Neural Dynamics to Behaviors through Asynchronous Communications,” Cognitive Neurodynamics, vol. 11, pp. 327-353, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Dennis V Christensen et al., “2022 Roadmap on Neuromorphic Computing and Engineering,” Neuromorphic Computing and Engineering, vol. 2, no. 2, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Martin Do Pham et al., “From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?,” Brain Sciences, vol. 13, no. 9, pp. 1-33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Dhriti Parikh et al., Neuromorphic Hardware and Computing, Medium, 2023. [Online]. Available: https://medium.com/@IEEE_Computer_Society_VIT/neuromorphic-hardware-and-computing-f7cc8f71ed58
[19] Spot -The Agile Mobile Robot, Boston Dynamics, 2023. [Online]. Available: https://bostondynamics.com/products/spot/