Feature Correlation Preserving Quantization for Analog Compute-in-Memory

Analog compute-in-memory (ACIM) enables efficient neural network inference by performing matrix operations within memory arrays, but its limited numerical precision requires low-bit quantization. Existing post-training quantization (PTQ) methods mainly minimize reconstruction error between full-precision and quantized representations. This objective does not explicitly preserve feature relations, which can be important when multiple features share the same quantization configuration within an ACIM mapping group. We propose Feature Correlation Preserving Quantization (FCPQ), a groupwise PTQ framework that preserves feature relations while optimizing reconstruction. FCPQ consists of two components. First, Feature Correlation Calibration (FCC) standardizes features and aligns the correlation matrices of full-precision and quantized representations within each mapping group. Second, Correlation Aware Quantization Optimization (CQO) estimates the contribution and distortion of feature relations and uses them to assign adaptive reconstruction weights. Together, these components guide quantization toward preserving both numerical values and feature relations. Experiments across multiple datasets and quantization settings show that FCPQ improves model accuracy and maintains the feature structure of full-precision models under low-bit quantization.