Proposal for a Leaky-Integrate-Fire Spiking Neuron Based on Magnetoelectric Switching of Ferromagnets

Abstract

The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing. Hardware implementations of a neuromorphic system aims to mimic the computations in the brain through interconnection of neurons and synaptic weights. A leaky-integrate-fire (LIF) spiking model is widely used to emulate the dynamics of neuronal action potentials. In this work, we propose a spin based LIF spiking neuron using the magneto-electric (ME) switching of ferro-magnets. The voltage across the ME oxide exhibits a typical leaky-integrate behavior, which in turn switches an underlying ferro-magnet. Due to the effect of thermal noise, the ferro-magnet exhibits probabilistic switching dynamics, which is reminiscent of the stochasticity exhibited by biological neurons. The energy-efficiency of the ME switching mechanism coupled with the intrinsic non-volatility of ferro-magnets result in lower energy consumption, when compared to a CMOS LIF neuron. A device to system-level simulation framework has been developed to investigate the feasibility of the proposed LIF neuron for a hand-written digit recognition problem.

Reservoir Computing Using Diffusive Memristors

“…These functions include, but are not limited to, paired pulse facilitation/paired pulse depression (PPF/PPD),long‐term potentiation/long‐term depression (LTP/LTD), and spike time dependent plasticity (STDP) . Recently, there have also been several reports of novel artificial neurons based on metal‐insulator‐transition (MIT, e.g., Nb 2 O 5 ,VO 2 ), ferromagnetic, phase change materials, as well as diffusive memristors . Accordingly, memristor‐based neural networks have been built following the tenets of ANN and a few that can be classified as SNN …”

Spiking neurons from tunable Gaussian heterojunction transistors

“…Leaky integrate and fire spiking neurons have also been achieved by combining a memristor with CMOS transistors 22 , but the number of necessary circuit elements remains large. In addition, leaky integrate and fire spiking neurons have been proposed using the magneto-electric effect 23 , but this implementation dissipates energy continuously, resulting in poor energy efficiency. A spiking neuron exploiting the abrupt state transition and hysteresis in ferroelectric field-effect transistors has also been shown 24 , but this approach is limited to spike frequency adaptation, whereas biological neurons exhibit a variety of other spiking behaviors (e.g., phasic and tonic spiking or bursting) 25 .…”

Various Threshold Switching Devices for Integrate and Fire Neuron Applications

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To implement a SNN using a hardware system, an integrate and fire (I&F) neuron is commonly adopted as a spiking neuron owing to its simplicity. [6] In this regard, volatile thershold switching (TS) devices [7][8][9][10][11] and nonvolatile memory such as resistive random access memory (RRAM) , [12] phase change random access memory (PRAM), [13] ferromagnetic material, [14] and floating body transistor [15] based I&F neurons have been reported to overcome the limitations of conventional CMOS-based neurons. When the membrane potential reaches the threshold voltage of the neuron, the neuron generates spikes to the next synapse layer and resets the membrane potential.

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