Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence

TL;DR

This paper presents ultra-low-power mixed-signal neuromorphic circuits using advanced FDSOI technology, enabling efficient real-time edge computing with biologically plausible neural dynamics.

Contribution

It introduces novel analog/digital circuits optimized for FDSOI processes to reduce power consumption in neuromorphic systems.

Findings

Circuit simulations show biologically plausible neural dynamics.

Designs are compact and suitable for large-scale neuromorphic processors.

Optimizations address analog design challenges in FDSOI technology.

Abstract

Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit simulation results and demonstrate the circuit's ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.