Comparing domain wall synapse with other non volatile memory devices for on-chip learning in analog hardware neural network

Abstract

Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices have been popularly used as synapses in crossbar array based analog Neural Network (NN) circuit to achieve more energy and time efficient data classification compared to conventional computers. Here we demonstrate the advantages of recently proposed spin orbit torque driven Domain Wall (DW) device as synapse compared to the RRAM and PCM devices with respect to on-chip learning (training in hardware) in such NN. Synaptic characteristic of DW synapse, obtained by us from micromagnetic modeling, turns out to be much more linear and symmetric (between positive and negative update) than that of RRAM and PCM synapse. This makes design of peripheral analog circuits for on-chip learning much easier in DW synapse based NN compared to that for RRAM and PCM synapses. We next incorporate the DW synapse as a Verilog-A model in the crossbar array based NN circuit we design on SPICE circuit simulator. Successful on-chip learning is demonstrated through SPICE simulations on the popular Fisher's Iris dataset. Time and energy required for learning turn out to be orders of magnitude lower for DW synapse based NN circuit compared to that for RRAM and PCM synapse based NN circuits.

On-chip learning of a domain-wall-synapse-crossbar-array-based convolutional neural network

“…The three-terminal domain-wall synapse device used here is based on the heavy metal/ferromagnetic metal hetero-structure, which exhibits perpendicular magnetic anisotropy (PMA), as shown in figure 1(a) [12,13,15,16,26,[41][42][43][44]. When in-plane 'write' current flows through the heavy-metal layer ('write' path, from terminal T3 to T1 in figure 1(a)), the domain wall present in the ferromagnetic free layer above it moves due to spin orbit torque experienced by the magnetic moments of the free layer at the interface with the heavy metal.…”

“…Among these non-volatile devices, the ferromagnetic domain-wall device (figure 1(a)), essentially a spintronic device, has been shown to provide a faster and more energy-efficient crossbar solution, for on-chip learning (training of the neural network on hardware itself), compared to other non-volatile memory devices, like resistive random access memory (RRAM) devices and phase change memory (PCM) devices [12][13][14][15][16][17][18]. This is because the domain-wall device has a much more linear and symmetric conductance response, or synaptic characteristic, compared to that of the RRAM or PCM device [16]. On-chip learning is known to provide several advantages for edge devices in terms of data security and the like [6,7].…”

Ferrimagnetic Synapse Devices for Fast and Energy-Efficient On-Chip Learning on An Analog-Hardware Neural Network

“…We also assume an interfacial Dzyalonshinskii Moriya Interaction (DMI) strength of 1.2 mJ/m 2 due to which the domain wall acquires Neel-type chirality. These simulation parameters have also been used in the experimentally benchmarked micromagnetic study of the heavy-metal/ferromagnet-bilayer device we consider here [21], [22], [46]. Fig.…”

Voltage-Controlled Energy-Efficient Domain Wall Synapses With Stochastic Distribution of Quantized Weights in the Presence of Thermal Noise and Edge Roughness

“…While the device variability is a persistent issue for all of the above-mentioned devices, recent work in fully connected artificial neural network (ANN) [8] shows equivalent accuracy to software-based training. Unfortunately, PCRAM and RRAM based devices consume energy on the order of a few pJs per synaptic weight alteration event [9]. Hence, the future IoTs and edge-devices where power is limited will necessitate alternate neuromorphic hardware that are energy efficient and enable real time programing of synaptic weights so the networks can be trained in-situ.…”

“…Recently, nanomagnet based synaptic devices has shown potential to be energy efficient compared to PCRAM and RRAM [9,10,11]. Among nanomagnet based neuromorphic devices, domain wall (DW) based MTJs are one of the most promising.…”