Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence.

Autor:	Jadaun P; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA., Cui C; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA., Liu S; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA., Incorvia JAC; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA.; Microelectronics Research Center, The University of Texas at Austin, Austin, TX 78758, USA.
Jazyk:	angličtina
Zdroj:	PNAS nexus [PNAS Nexus] 2022 Sep 29; Vol. 1 (5), pp. pgac206. Date of Electronic Publication: 2022 Sep 29 (Print Publication: 2022).
DOI:	10.1093/pnasnexus/pgac206
Abstrakt:	Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain's intellectual abilities. An impressive ability of the brain is its adaptive intelligence, which allows the brain to regulate its functions "on the fly" to cope with myriad and ever-changing situations. In particular, the brain displays three adaptive and advanced intelligence abilities of context-awareness, cross frequency coupling, and feature binding. To mimic these adaptive cognitive abilities, we design and simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion lattice, thereby modulating the neuron's state, its dynamics and its transfer function "on the fly." This adaptive neuron is used to demonstrate the three cognitive abilities, of which context-awareness and cross-frequency coupling have not been previously realized in hardware neurons. Additionally, the neuron is used to construct an adaptive artificial neural network (ANN) and perform context-aware diagnosis of breast cancer. Simulations show that the adaptive ANN diagnoses cancer with higher accuracy while learning faster and using a more compact and energy-efficient network than a nonadaptive ANN. The work further describes how hardware-based adaptive neurons can mitigate several critical challenges facing contemporary ANNs. Modern ANNs require large amounts of training data, energy, and chip area, and are highly task-specific; conversely, hardware-based ANNs built with adaptive neurons show faster learning, compact architectures, energy-efficiency, fault-tolerance, and can lead to the realization of broader artificial intelligence. (© The Author(s) 2022. Published by Oxford University Press on behalf of National Academy of Sciences.)
Databáze:	MEDLINE
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