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<p>Sorting is a fundamental task in modern computing systems. Hardware sorters are typically based on the von Neumann architecture, and their performance is limited by the data transfer bandwidth and CMOS memory. Sort-in-memory using memristors could help overcome these limitations, but current systems still rely on comparison operations so that sorting performance remains limited. Here we describe a fast and reconfigurable sort-in-memory system that uses digit reads of one-transistor–one-resistor memristor arrays. We develop digit-read tree node skipping, which supports various data quantities and data types. We extend this approach with the multi-bank, bit-slice and multi-level strategies for cross-array tree node skipping. We experimentally show that our comparison-free sort-in-memory system can improve throughput by ×7.70, energy efficiency by ×160.4 and area efficiency by ×32.46 compared with conventional sorting systems. To illustrate the potential of the approach to solve practical sorting tasks, as well as its compatibility with other compute-in-memory schemes, we apply it to Dijkstra’s shortest path search and neural network inference with in situ pruning.</p>

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A fast and reconfigurable sort-in-memory system based on memristors

  • 期刊论文

  • 发表日期：

    2025年6月25日

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  • Lianfeng Yu,
  • Teng Zhang,
  • Zeyu Wang,
  • Xile Wang,
  • Zelun Pan,
  • Bowen Wang,
  • Zhaokun Jing,
  • Jiaxin Liu,
  • Yuqi Li,
  • Ziang Xie,
  • Yihang Zhu,
  • Bonan Yan,
  • Yaoyu Tao,
  • Yuchao Yang

摘要

Sorting is a fundamental task in modern computing systems. Hardware sorters are typically based on the von Neumann architecture, and their performance is limited by the data transfer bandwidth and CMOS memory. Sort-in-memory using memristors could help overcome these limitations, but current systems still rely on comparison operations so that sorting performance remains limited. Here we describe a fast and reconfigurable sort-in-memory system that uses digit reads of one-transistor–one-resistor memristor arrays. We develop digit-read tree node skipping, which supports various data quantities and data types. We extend this approach with the multi-bank, bit-slice and multi-level strategies for cross-array tree node skipping. We experimentally show that our comparison-free sort-in-memory system can improve throughput by ×7.70, energy efficiency by ×160.4 and area efficiency by ×32.46 compared with conventional sorting systems. To illustrate the potential of the approach to solve practical sorting tasks, as well as its compatibility with other compute-in-memory schemes, we apply it to Dijkstra’s shortest path search and neural network inference with in situ pruning.