Memristor-based adaptive analog-to-digital conversion for efficient and accurate compute-in-memory

Abstract

Abstract Compute-in-memory technology offers promising solutions for neural network acceleration but its potential is severely limited by inflexible and resource-intensive analog-to-digital converters. Here, we present a memristor-based analog-to-digital converter featuring adaptive quantization for diverse output distributions. Our design employs analog content-addressable memory cells with programmable overlapped boundaries to establish optimized quantization thresholds, demonstrating excellent integral and differential non-linearities. Extensive experiments validate the robustness of our approach by achieving 89.55% accuracy on CIFAR-10 (VGG8) at 5-bit adaptive quantized precision and maintaining competitive performance on ImageNet (ResNet18) through a proposed super-resolution strategy under experimental memristor variations. Compared to state-of-the-art designs, our converter achieves a 15.1× improvement in energy efficiency and a 12.9× reduction in area. Furthermore, integrating our converter into CIM systems reduces the energy and area overhead by up to 57.2% and 30.7%, respectively. This work establishes a paradigm for efficient and accurate signal quantization in practical compute-in-memory systems.