Adiabatic leaky integrate and fire neurons with refractory period for ultra low energy neuromorphic computing
Year: 2024
Type: article
Abstract: In recent years, the in-memory-computing in charge domain has gained significant interest as a promising solution to further enhance the energy efficiency of neuromorphic hardware. In this work, we explore the synergy between the brain-inspired computation and the adiabatic paradigm by presenting an adiabatic Leaky Integrate-and-Fire neuron in 180 nm CMOS technology, that is able to emulate the most important primitives for a valuable neuromorphic computation, such as the accumulation of the incoming input spikes, an exponential leakage of the membrane potential and a tunable refractory period. Differently from previous contributions in the literature, our design can exploit both the charging and recovery phases of the adiabatic operation to ensure a seamless and continuous computation, all the while exchanging energy with the power supply with an efficiency higher than 90% over a wide range of resonance frequencies, and even surpassing 99% for the lowest frequencies. Our simulations unveil a minimum energy per synaptic operation of 470 fJ at a 500 kHz resonance frequency, which yields a 9x energy saving with respect to a non-adiabatic operation.
Source: npj Unconventional Computing
Institution: University of Udine
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
Award: G119612188
OpenAlex ID: W4404996498