Extended Data Fig. 7: Energy model construction.
From: A lossless and fully parallel spintronic compute-in-memory macro for artificial intelligence chips
In neural network workloads, convolution layers are mapped onto the nvDCIM hardware to perform efficient MVM operation, with feature maps and kernels assigned to input drivers and memory banks. Dynamic energy consumption is calculated by aggregating the energy costs of IBMD-bitcells, input drivers, and adders based on precomputed energy look-up tables. This model enables an evaluation of both model accuracy and energy consumption across convolutional and fully connected layers.
