An Energy Consumption Model for SRAM-Based In-Memory-Computing Architectures - Akgul, Berke; Karalar, Tufan Coskun (MDPI, 2024)
In this paper, a mathematical model for obtaining energy consumption of IMC architectures is constructed. This model provides energy estimation based on the distribution of a specific dataset. In addition, the estimation reduces the required simulation time to create an energy consumption model of SRAM-based IMC architectures. To validate our model with realistic data, the energy consumption of IMC is compared by using NeuroSim V3.0 for the CIFAR-10 and MNIST-like datasets. Furthermore, an application is created with our model to select highest performing quantization mapping based upon the parameters of energy consumption and accuracy.
Keywords: in-memory computing; benchmarking; energy consumption; mathematical model; CNN ACCELERATOR; PRECISION; Computer Science; Engineering; Physics