A low-voltage, low power STDP synapse implementation using domain-wall magnets for spiking neural networks

Online, real-time learning in neuromorphic circuits have been implemented through variants of Spike Time Dependent Plasticity (STDP). Current implementations have used either floating-gate devices or memristors to implement such learning synapses together with non-volatile storage. However, these approaches require high voltages (≈ 3-12V) for weight update and entail high energy for learning (≈ 4-30pJ/write). We present a domain wall memory based low-voltage, low-energy STDP synapse that can operate with a power supply as low as 0.8V and update the weight at ≈ 40fJ/write. Device level simulations are performed to prove its feasibility. Its use in associative learning is also demonstrated by using neurons with dendritic branches to classify spike patterns from MNIST dataset.

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