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

Abstract

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 non-adaptive 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.

Review of Orbital Magnetism in Graphene-Based Moiré Materials

“…Thus, experimental reports repeatedly show ultra-low-power electric control of the orbital magnetic state in moiré heterostructures, indicating the exceptional promise of moiré materials for ultra-low-power spintronic and magnetic applications [37]. The experimental realization of magnetic skyrmions in moiré heterostructures, as predicted in [74], can open up novel pathways for the implementation of advanced spintronic neuromorphic computing devices [75].…”

Magnetic skyrmions and domain walls for logical and neuromorphic computing

“…Jadaun et al exploited the flexibility of coupled skyrmions to create a reconfigurable, oscillatory neuron reminiscent of the adaptiveness of the brain [105]. The proposed neuron, named the Tunable SKyrmion Oscillatory Neuron (T-SKONE), consists of a two-dimensional skyrmion array sitting on artificial soft pinning sites; current-driven skyrmion motion allows the skyrmion lattice to be transformed between two different configurations (figure 7).…”

“…In medical diagnosis, not only are biopsy data considered but also the 'context' of the patient; in this case, habits ('risk factors') or medical history. To demonstrate their computational power, a two-layer feed-forward ANN consisting of T-SKONEs was designed in [105]. T-SKONEs in the input layer take input x that encodes input features and control or contextual input y that reconfigures the neuron input-output characteristics.…”

Choose your tools carefully: a comparative evaluation of deterministic vs. stochastic and binary vs. analog neuron models for implementing emerging computing paradigms

“…Spintronics devices can be implemented with low energy and high density and are compatible with existing CMOS technology (Sengupta et al, 2016a). The spintronics devices utilized in neuromorphic computing include spin-torque devices (Torrejon et al, 2017;Roy et al, 2014;Sengupta et al, 2016b), magnetic domain walls (Siddiqui et al, 2020;Leonard et al, 2022;Brigner et al, 2022), and skyrmions (Jadaun et al, 2022;Song et al, 2020). Optical or photonics devices are also implemented for neurons and synapses in recent years (Shastri et al, 2021;Romeira et al, 2016;Guo et al, 2021).…”