  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.
