LaMoS：通过基于SRAM的CiM加速实现高效的大数模乘
LaMoS: Enabling Efficient Large Number Modular Multiplication through SRAM-based CiM Acceleration
AI总结：
针对现有CiM方案在大数模乘中位宽不足和扩展成本高的问题，提出基于SRAM的高效CiM架构LaMoS，通过优化映射和数据流实现7.02倍加速并降低扩展成本。
摘要：
Barrett算法是执行模乘最广泛使用的方法之一，模乘是同态加密（HE）和零知识证明（ZKP）等现代隐私计算技术中关键的非线性运算。由于模乘在这些应用中占据了主要的处理时间，计算复杂度和内存限制显著影响了性能。存内计算（CiM）是解决这一问题的一种有前景的方法。然而，现有方案目前存在两个主要问题：1）大多数工作关注低位宽模乘，这对于椭圆曲线密码（ECC）和RSA算法等主流密码算法来说是不够的，这两者都需要高位宽运算；2）针对大数模乘的近期工作依赖于低效的存内逻辑运算，导致更大位宽的扩展成本高且延迟增加。为了解决这些问题，我们提出了LaMoS，一种用于大数模乘的基于SRAM的高效CiM设计，提供高可扩展性和面积效率。首先，我们分析了Barrett模乘方法，并将工作负载映射到SRAM CiM宏上以处理高位宽情况。此外，我们开发了高效的CiM架构和数据流以优化大数模乘。最后，我们通过工作负载分组改进了映射方案，以在高位宽场景下实现更好的可扩展性。实验结果表明，与现有的基于SRAM的CiM设计相比，LaMoS实现了7.02倍的加速，并降低了高位宽的扩展成本。
英文摘要：
Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). Since modular multiplication dominates the processing time in these applications, computational complexity and memory limitations significantly impact performance. Computing-in-Memory (CiM) is a promising approach to tackle this problem. However, existing schemes currently suffer from two main problems: 1) Most works focus on low bit-width modular multiplication, which is inadequate for mainstream cryptographic algorithms such as elliptic curve cryptography (ECC) and the RSA algorithm, both of which require high bit-width operations; 2) Recent efforts targeting large number modular multiplication rely on inefficient in-memory logic operations, resulting in high scaling costs for larger bit-widths and increased latency. To address these issues, we propose LaMoS, an efficient SRAM-based CiM design for large-number modular multiplication, offering high scalability and area efficiency. First, we analyze the Barrett's modular multiplication method and map the workload onto SRAM CiM macros for high bit-width cases. Additionally, we develop an efficient CiM architecture and dataflow to optimize large-number modular multiplication. Finally, we refine the mapping scheme for better scalability in high bit-width scenarios using workload grouping. Experimental results show that LaMoS achieves a $7.02\times$ speedup and reduces high bit-width scaling costs compared to existing SRAM-based CiM designs.
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