  In the quest for low power, bio-inspired computation both memristive and
memcapacitive-based Artificial Neural Networks (ANN) have been the subjects of
increasing focus for hardware implementation of neuromorphic computing. One
step further, regenerative capacitive neural networks, which call for the use
of adiabatic computing, offer a tantalising route towards even lower energy
consumption, especially when combined with `memimpedace' elements. Here, we
present an artificial neuron featuring adiabatic synapse capacitors to produce
membrane potentials for the somas of neurons; the latter implemented via
dynamic latched comparators augmented with Resistive Random-Access Memory
(RRAM) devices. Our initial 4-bit adiabatic capacitive neuron proof-of-concept
example shows 90% synaptic energy saving. At 4 synapses/soma we already witness
an overall 35% energy reduction. Furthermore, the impact of process and
temperature on the 4-bit adiabatic synapse shows a maximum energy variation of
30% at 100 degree Celsius across the corners without any functionality loss.
Finally, the efficacy of our adiabatic approach to ANN is tested for 512 & 1024
synapse/neuron for worst and best case synapse loading conditions and variable
equalising capacitance's quantifying the expected trade-off between
equalisation capacitance and range of optimal power-clock frequencies vs.
loading (i.e. the percentage of active synapses).
