LODeNNS: A Linearly-approximated and Optimized Dendrocentric Nearest Neighbor STDP

Abstract

Realizing Hebbian plasticity in large-scale neuromorphic systems is essential for reconfiguring them for recognition tasks. Spike-timing-dependent plasticity, as a tool to this effect, has received a lot of attention in recent times. This phenomenon encodes weight update information as correlations between the presynaptic and postsynaptic event times, as such, it is imperative for each synapse in a silicon neural network to somehow keep its own time. We present a biologically plausible and optimized Register Transfer Level (RTL) and algorithmic approach to the Nearest-Neighbor STDP with time management handled by the postsynaptic dendrite. We adopt a time-constant based ramp approximation for ease of RTL implementation and incorporation in large-scale digital neuromorphic systems.