Building time-surfaces by exploiting the complex volatility of an ECRAM memristor
arxiv(2022)
摘要
Memristors have emerged as a promising technology for efficient neuromorphic
architectures owing to their ability to act as programmable synapses, combining
processing and memory into a single device. Although they are most commonly
used for static encoding of synaptic weights, recent work has begun to
investigate the use of their dynamical properties, such as Short Term
Plasticity (STP), to integrate events over time in event-based architectures.
However, we are still far from completely understanding the range of possible
behaviors and how they might be exploited in neuromorphic computation. This
work focuses on a newly developed Li_xWO_3-based
three-terminal memristor that exhibits tunable STP and a conductance response
modeled by a double exponential decay. We derive a stochastic model of the
device from experimental data and investigate how device stochasticity, STP,
and the double exponential decay affect accuracy in a hierarchy of
time-surfaces (HOTS) architecture. We found that the device's stochasticity
does not affect accuracy, that STP can reduce the effect of salt and pepper
noise in signals from event-based sensors, and that the double exponential
decay improves accuracy by integrating temporal information over multiple time
scales. Our approach can be generalized to study other memristive devices to
build a better understanding of how control over temporal dynamics can enable
neuromorphic engineers to fine-tune devices and architectures to fit their
problems at hand.
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