  Vector-matrix-multiplication (VMM) accel-erators have gained a lot of
traction, especially due to therise of convolutional neural networks (CNNs) and
the desireto compute them on the edge. Besides the classical digitalapproach,
analog computing has gone through a renais-sance to push energy efficiency
further. A more recent ap-proach is called time-domain (TD) computing. In
contrastto analog computing, TD computing permits easy technol-ogy as well as
voltage scaling. As it has received limitedresearch attention, it is not yet
clear which scenarios aremost suitable to be computed in the TD. In this work,
weinvestigate these scenarios, focussing on energy efficiencyconsidering
approximative computations that preserve ac-curacy. Both goals are addressed by
a novel efficiency met-ric, which is used to find a baseline design. We use
SPICEsimulation data which is fed into a python framework toevaluate how
performance scales for VMM computation.We see that TD computing offers best
energy efficiency forsmall to medium sized arrays. With throughput and sili-con
footprint we investigate two additional metrics, givinga holistic comparison.
