Estimating dynamic power consumption for memristor-based CiM architecture

Nowadays, Computing-in-Memory (CiM) represents one of the most relevant solutions to deal with CMOS technological issues and several works have been proposed so far targeting front and back-end synthesis. However, a given CiM architecture can be synthesized depending on different parameters, leading to different implementations w.r.t. area, power consumption and performance. It is thus mandatory to have an evaluation framework to characterize the actual implementation depending on the above terms. This is even more important during the Design Exploration phase, in which many different implementations are explored to identify the best candidate w.r.t. the user requirements. In this work, we focus on the dynamic power consumption estimation of a given CiM implementation. Instead of resorting to a simulation-based power estimation, we propose an analytical approach that will dramatically speed up the estimation since no simulations are required. By comparing the proposed approach against the simulation-based method over a massive experimental campaign, we show that the accuracy of the estimation turns out to be very high.