Mimicking locust brains
Researchers (IIT Bombay and Kings College) have built two-dimensional materials-based transistors and used them to design ultra-low power artificial neuron circuits for autonomous robots
In the age of artificial intelligence (AI), there are many exciting developments from chatbots powered by large-language models to autonomous vehicles and self-driving cars. We are in a very exciting phase of continuously evolving AI, witnessing how these innovations are unfolding and impacting our lives. While Tesla’s self-driving cars have hit the market in many other countries, India’s own Pragyan rover (built by ISRO) navigated itself on the unchartered surface of the moon.
One of the key challenges in autonomous vehicles is the ability to accurately and quickly detect moving obstacles. The existing obstacle detection systems, based on complex algorithms and vision systems, are often inefficient in terms of energy consumption and size. In a recent study, researchers from the Indian Institute of Technology Bombay (IIT Bombay) and King’s College London, United Kingdom have designed and built an ultra-low power transistor, which when incorporated into their artificial neuron circuit design, is capable of obstacle detection. The circuit mimics the spiking neuron model of biological neurons.
The researchers were motivated by the brain’s unique ability to process information in a distinctive manner. Particularly, they took note of the behaviour of a collision-detecting neuron found in locusts. The neuron, called lobula giant movement detector (LGMD), plays a crucial role in helping locusts avoid collisions with objects in their path. The mechanism is similar to the way a computer works, but the brain does it in a much more energy-efficient way. In the current study, the team has designed a new type of low-power artificial neuron circuit that closely mimics the behaviour of this collision-detecting neuron found in locusts.
The novel artificial neuron circuit is designed by incorporating the models of a new subthreshold transistor built using a two-dimensional (2D) material channel. The use of ultra-thin 2D materials allows reconfigurable and low-power operation, making it suitable for energy-efficient applications. The transistor was carefully designed and fabricated to replicate sodium channel behaviour in biological neurons besides operating under a low-current regime, which enhances its energy efficiency.
Prof Bipin Rajendran, Department of Engineering, King’s College London, and a co-author of this study, says “We demonstrated that this spiking neuron circuit can be used for obstacle detection. However, the circuit can be used in other neuromorphic (systems mimicking the human brain) applications based on analog or mixed signal technology that require a low-energy spiking neuron.”
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