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<p>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.</p>

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Achieving high precision in analog in-memory computing systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Piergiulio Mannocci,
  • Giacomo Larelli,
  • Marco Bonomi,
  • Daniele Ielmini

摘要

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.