Achieving high precision in analog in-memory computing systems

Modern workloads challenge von Neumann architectures due to memory-processor data transfer. In-memory computing (IMC) enables in situ processing, with analog IMC (AIMC) based on resistive memories offering high-throughput and energy-efficient multiply-accumulate operations. Precision is limited by noise, device/circuit variations, and non-idealities. This work reviews error sources in AIMC and surveys mitigation strategies: bit slicing, residue number system, error correction codes, and mixed-precision iterative refinement, analyzing hardware implementations, overheads, and tradeoffs.