| Abstrakt: |
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 ($ \approx 170{\kern 1pt} {\mathrm{n}}{{\mathrm{m}}^2}$≈170nm2) and operate at low voltage ($200{\kern 1pt} {\mathrm{mV}}$200 mV ). When compared with a biological brain synapse, its energy consumption per spike for a weight update operation ($0.5{\kern 1pt} {\mathrm{fJ}}$0.5 fJ ) is $20 \times $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. |
