Science and Technology

Spiking neuron for speedy, accurate AI

| Updated on July 18, 2021

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The human brain is one of the most powerful and intelligent natural computers known to humankind. Neuromorphic computing refers to the field of technology where engineers try to build intelligent machines inspired from the working of mammal brains. Neurons and synapses are believed to be the most important building blocks giving rise to intelligence inside brains.

Researchers at IIT-Delhi, led by Prof Manan Suri, Department of Electrical Engineering, have invented a new spiking neuron model named DEXAT (Double EXponential Adaptive Threshold). The invention is significant as it will help build accurate, fast and energy-efficient neuromorphic artificial intelligence (AI) systems for real-world applications like speech recognition.

The work, being interdisciplinary, lies at the intersection of AI, neuromorphic hardware and nanoelectronics.

“We have successfully demonstrated the utilisation of memory technology beyond simple storage. We have efficiently utilised semiconductor memory for applications such as in-memory computing, neuromorphic computing, edge-AI, sensing and hardware security. This work specifically exploits analogue properties of nanoscale oxide-based memory devices for building adaptive spiking neurons,” Suri says in an IIT-Delhi press release.

The study demonstrated a neuron model with higher accuracy, faster convergence and flexibility in hardware implementation compared to other state-of-the-art adaptive threshold spiking neurons. The proposed solution achieves high performance with fewer neurons. The benefits of the proposed invention were shown on multiple datasets.

The scientists successfully demonstrated a hybrid nanodevice-based hardware realisation. The proposed nanodevice neuromorphic network was found to achieve 94 per cent accuracy even with very high device variability, indicating robustness.

Published on July 18, 2021

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