Mind-on-a-chip would want little coaching

Advances in synthetic intelligence expertise is resulting in the event of neural networks that mimic the biology of the mind. Credit score: KAUST

A neural community that mimics the biology of the mind could be loaded onto a microchip for sooner and extra environment friendly synthetic intelligence.

A biomimicking “spiking” neural community on a microchip has enabled KAUST researchers to put the muse for creating extra environment friendly hardware-based synthetic intelligence computing techniques.

Synthetic intelligence expertise is creating quickly, with an explosion of recent purposes throughout superior automation, information mining and interpretation, healthcare and advertising and marketing, to call just a few. Such techniques are based mostly on a mathematical synthetic neural community (ANN) composed of layers of decision-making nodes. Labeled information is first fed into the system to “prepare” the mannequin to reply a sure method, then the decision-making guidelines are locked in and the mannequin is put into service on customary computing {hardware}.

Whereas this methodology works, it’s a clunky approximation of the much more advanced, highly effective and environment friendly neural community that really makes up our brains.

“An ANN is an summary mathematic mannequin that bears little resemblance to actual nervous techniques and requires intensive computing energy,” says Wenzhe Guo, a Ph.D. scholar within the analysis group. “A spiking neural community, however, is constructed and works in the identical method because the organic nervous system and may course of data in a sooner and extra energy-efficient method.”

Spiking neural networks (SNNs) emulate the construction of the nervous system as a community of synapses that transmit data by way of ion channels within the type of motion potential, or spikes, as they happen. This event-driven habits, applied mathematically as a “leaky integrate-and-fire mannequin,” makes SNNs very power environment friendly. Plus, the construction of interconnected nodes supplies a excessive diploma of parallelization, which additional boosts processing energy and effectivity. It additionally lends itself to implementation instantly in computing {hardware} as a neuromorphic chip.

“We used an ordinary low-cost FPGA microchip and applied a spike-timing-dependent plasticity mannequin, which is a organic studying rule found in our mind,” says Guo.

Importantly, this organic mannequin doesn’t want instructing indicators or labels, permitting the neuromorphic computing system to study real-world information patterns with out coaching.

“Since SNN fashions are very advanced, our principal problem was to tailor the neural community settings for optimum efficiency,” says Guo. “We then designed the optimum {hardware} structure contemplating a stability of value, velocity and power consumption.”

The group’s brain-on-a-chip proved to be greater than 20 occasions sooner and 200 occasions extra power environment friendly than different neural community platforms.

“Our final objective is to construct a compact, quick and low-energy brain-like {hardware} computing system. The following step is to enhance the design and optimize product packaging, miniaturize the chip and customise it for numerous industrial purposes via collaboration,” Guo says.


New strategy discovered for energy-efficient AI purposes


Extra data:
Wenzhe Guo et al. Towards the Optimum Design and FPGA Implementation of Spiking Neural Networks, IEEE Transactions on Neural Networks and Studying Programs (2021). DOI: 10.1109/tnnls.2021.3055421

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King Abdullah College of Science and Expertise


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Mind-on-a-chip would want little coaching (2021, April 20)
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