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.

翻译：向量-矩阵乘法（VMM）加速器已获得广泛关注，尤其是由于卷积神经网络（CNN）的兴起以及在其边缘端进行计算的需求。除传统的数字方法外，模拟计算迎来了复兴，以进一步提升能效。一种较新的方法被称为时域（TD）计算。与模拟计算相比，时域计算便于实现技术缩放和电压缩放。由于该领域研究关注度有限，目前尚不明确哪些场景最适合采用时域计算。在本研究中，我们针对这些场景展开探讨，重点关注在保持精度的前提下实现近似计算能效的优化。两个目标通过一种新型效率指标得以实现，该指标用于确定基线设计。我们采用SPICE仿真数据，并将其输入至Python框架，以评估VMM计算的性能扩展情况。研究发现，时域计算在中小规模阵列中具有最佳能效。此外，我们还从吞吐量和硅片面积两个额外指标出发，进行了全面比较。