• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2025, Vol. 47 ›› Issue (9): 1555-1562.

• 高性能计算 • 上一篇    下一篇

面向偏微分方程求解的存内计算电路及宏单元设计

王景可,谢艾森,常亮   

  1. (电子科技大学信息与通信工程学院,四川 成都 611731)

  • 收稿日期:2024-12-31 修回日期:2025-02-15 出版日期:2025-09-25 发布日期:2025-09-22
  • 基金资助:
    国家自然科学基金(62104025)

Computing-in-memory circuit and macro design for solving partial differential equations

WANG Jingke,XIE Aisen,CHANG Liang   

  1. (School of Information and Communication Engineering,
    University of Electronic Science  and Technology of China,Chengdu 611731,China)

  • Received:2024-12-31 Revised:2025-02-15 Online:2025-09-25 Published:2025-09-22

摘要: 为了应对在自然科学和工程领域中遇到的高精度偏微分方程求解的计算挑战,提出了一种基于存内计算(CIM)架构的新型偏微分方程求解系统。该求解系统基于存内计算技术,通过将计算逻辑直接嵌入存储器中,显著减少了处理器与存储器间的数据传输需求。详细分析了偏微分方程求解的计算过程,提取关键的计算流程,并转化为适合于存内计算的矩阵乘法和累加运算。通过设计针对CIM架构的并行计算方案和相应的行为级模型,进一步开发和测试了硬件实现方案。通过与传统CPU的计算结果进行对比,验证了所提设计的正确性和高效性。 实验结果显示,在处理二维泊松方程和波动方程等偏微分方程时,所提设计对于二维方程的求解精度超过98%,对于一维方程的求解精度达到99.8%,并且求解速度相比CPU的提高了76倍。

关键词: 存内计算, 偏微分方程, 矩阵迭代算法

Abstract: To address the computational challenges of high-precision partial differential equation (PDE) solving in natural sciences and engineering,this paper proposes a novel PDE solver based on the computing-in-memory (CIM) architecture.Leveraging CIM technology,the solver embeds computational logic directly into memory,significantly reducing data transmission between the processor and memory.We thoroughly analyze the computational process of PDE solving,extract key computational flows,and transform them into matrix multiplication and accumulation operations suitable for CIM.By designing a parallel computing scheme and corresponding behavior-level model for the CIM architecture,we further develop and test the hardware implementation.The correctness and efficiency of the proposed design are verified by comparing results with traditional CPU computations.Experimental results show that when solving 2D Poisson equations,wave equations,and other PDEs,the solver achieves a solution accuracy of over 98% for 2D equations and 99.8% for 1D equations,with a solution speed 76 times faster than that of CPUs.

Key words: computing-in-memory, partial differential equation, matrix iteration algorithm