Genetic Algorithm-Driven IMC Mapping for CNNs Using Mixed Quantization and MLC FeFETs

Ferroelectric Field-Effect Transistors (FeFETs) are emerging as a highly promising non-volatile memory (NVM) technology for in-memory computing architectures, thanks to their low power consumption and non-volatility. These characteristics make FeFETs particularly well-suited for convolutional neural networks (CNNs), especially in power-constrained environments where minimizing the memory footprint is critical for improving both area efficiency and energy consumption. Two effective strategies for reducing memory requirements are quantization and the use of multi-level cell (MLC) configurations in NVMs. This work proposes a solution that combines mixed quantization schemes with FeFET-based MLC and single-level cell (SLC) configurations to balance memory usage and accuracy. Given the large hyperparameter space introduced by these combinations, we employ a genetic algorithm to efficiently explore and identify Pareto-optimal solutions, allowing flexible adaptation to various application-specific requirements. Our approach achieves significant improvements in both memory efficiency and performance, reducing memory usage by 50% while sacrificing only 3% accuracy compared to the 8-bit ResNet baseline. After a single epoch of retraining, the accuracy matches the baseline while fully retaining the memory savings. Additionally, when compared to the 4-bit baseline, a 46% memory reduction is achieved with virtually no loss in accuracy.