Low Energy, Non-Cortical, Graphene Nanoribbon-Based STDP Plastic Synapses
N Cucu-Laurenciu (TU Delft - Computer Engineering, Radboud Universiteit Nijmegen)
Charles Timmermans (Radboud Universiteit Nijmegen)
Shao Ku Kao Cotofana (TU Delft - Computer Engineering, TU Delft - Quantum & Computer Engineering)
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Abstract
The realization of energy efficient, low area, and fast processing neuron and synapse circuits is of prime importance for unleashing neuromorphic computing full potential. In this paper, we introduce a graphene-based synapse, which can emulate Spike Timing Dependent Plasticity (STDP) and Short/Long Term Plasticity (STP/LTP) with variable signal amplitude and temporal dynamics. The synapse operation is validated by means of SPICE simulations, and its synaptic modulation ability is showcased through reinforcement learning within a Spiking Neural Network for robotic navigation with obstacles avoidance. Besides its functional versatility, the proposed graphene-based synapse can potentially occupy low active area (≈ 170nm2) and operate at low voltage (200 mV ). When compared with a biological brain synapse, its energy consumption per spike for a weight update operation (0.5 fJ ) is 20 × - lower, while the processing speed is increased by six orders of magnitude. Such properties are essential desiderata for the realization of large scale neuromorphic systems, making the proposed graphene-based synapse an outstanding candidate for this purpose.