Fold-PIM: A Cost-Efficient LPDDR5-Based PIM for On-Device SLMs

IF 1.4 3区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

IEEE Computer Architecture Letters Pub Date : 2025-03-02 DOI:10.1109/LCA.2025.3566692

Kyoungho Jeun;Hyeonu Kim;Eojin Lee

引用次数: 0

Abstract

The increasing demand for on-device AI applications has shifted focus to Small Language Models (SLMs) optimized for mobile environments. However, the limited memory bandwidth of LPDDR5-based systems presents significant challenges for efficiently executing memory-bound matrix-vector multiplication operations, a core component of SLM inference. In this paper, we propose Fold-PIM, an LPDDR5-based Processing-in-Memory (PIM) architecture designed to address these challenges. Fold-PIM features a shared PU architecture that leverages subarray-level parallelism and employs key techniques with in-tile transposition, adaptive tiling, and a tailored protocol to reduce vector replacement latency. Our evaluation results demonstrate that Fold-PIM achieves up to 3.9× speedup of token generation time in SLM inference compared to the baseline system without PIM.

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Fold-PIM：一种基于lpddr5的低成本器件级slm PIM

对设备上人工智能应用日益增长的需求已经将重点转移到针对移动环境优化的小语言模型（slm）上。然而，基于lpddr5的系统有限的内存带宽对有效执行内存约束的矩阵向量乘法运算（SLM推理的核心组件）提出了重大挑战。在本文中，我们提出Fold-PIM，一种基于lpddr5的内存中处理（PIM）架构，旨在解决这些挑战。Fold-PIM的特点是一个共享的PU架构，它利用了子阵列级的并行性，并采用了一些关键技术，如块内移位、自适应平铺和定制协议，以减少向量替换延迟。我们的评估结果表明，与没有PIM的基准系统相比，Fold-PIM在SLM推理中的令牌生成时间加快了3.9倍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Computer Architecture Letters COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-

CiteScore

4.60

自引率

4.30%

发文量

期刊介绍： IEEE Computer Architecture Letters is a rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessor computer systems, computer architecture, microarchitecture, workload characterization, performance evaluation and simulation techniques, and power-aware computing. Submissions are welcomed on any topic in computer architecture, especially but not limited to: microprocessor and multiprocessor systems, microarchitecture and ILP processors, workload characterization, performance evaluation and simulation techniques, compiler-hardware and operating system-hardware interactions, interconnect architectures, memory and cache systems, power and thermal issues at the architecture level, I/O architectures and techniques, independent validation of previously published results, analysis of unsuccessful techniques, domain-specific processor architectures (e.g., embedded, graphics, network, etc.), real-time and high-availability architectures, reconfigurable systems.