Jeon Sangmin, Lee Kangju, Lee Kyeongwon, Lee Woojoo
Department of Intelligent Semiconductor Engineering, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.
Micromachines (Basel). 2024 Sep 30;15(10):1222. doi: 10.3390/mi15101222.
The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI applications on edge devices requires a careful balance between data processing performance and energy efficiency. This challenge becomes even more critical when the computational load of applications dynamically changes over time, making it difficult to maintain optimal performance and energy efficiency simultaneously. To address these challenges, we propose a novel processing-in-memory (PIM) technology that dynamically optimizes performance and power consumption in response to real-time workload variations in AI applications. Our proposed solution consists of a new PIM architecture and an operational algorithm designed to maximize its effectiveness. The PIM architecture follows a well-established structure known for effectively handling data-centric tasks in AI applications. However, unlike conventional designs, it features a heterogeneous configuration of high-performance PIM (HP-PIM) modules and low-power PIM (LP-PIM) modules. This enables the system to dynamically adjust data processing based on varying computational load, optimizing energy efficiency according to the application's workload demands. In addition, we present a data placement optimization algorithm to fully leverage the potential of the heterogeneous PIM architecture. This algorithm predicts changes in application workloads and optimally allocates data to the HP-PIM and LP-PIM modules, improving energy efficiency. To validate and evaluate the proposed technology, we implemented the PIM architecture and developed an embedded processor that integrates this architecture. We performed FPGA prototyping of the processor, and functional verification was successfully completed. Experimental results from running applications with varying workload demands on the prototype PIM processor demonstrate that the proposed technology achieves up to 29.54% energy savings.
人工智能（AI）技术的迅速发展，再加上物联网（IoT）设备的广泛普及，显著扩大了AI应用的范围，从数据中心到边缘设备。在边缘设备上运行AI应用需要在数据处理性能和能源效率之间仔细权衡。当应用的计算负载随时间动态变化时，这一挑战变得更加关键，使得难以同时维持最佳性能和能源效率。为应对这些挑战，我们提出了一种新颖的内存处理（PIM）技术，该技术可根据AI应用中的实时工作负载变化动态优化性能和功耗。我们提出的解决方案包括一种新的PIM架构和一种旨在最大化其有效性的操作算法。该PIM架构遵循一种成熟的结构，以有效处理AI应用中以数据为中心的任务而闻名。然而，与传统设计不同的是，它具有高性能PIM（HP-PIM）模块和低功耗PIM（LP-PIM）模块的异构配置。这使系统能够根据变化的计算负载动态调整数据处理，根据应用的工作负载需求优化能源效率。此外，我们提出了一种数据放置优化算法，以充分利用异构PIM架构的潜力。该算法预测应用工作负载的变化，并将数据最优地分配到HP-PIM和LP-PIM模块，提高能源效率。为了验证和评估所提出的技术，我们实现了PIM架构并开发了集成该架构的嵌入式处理器。我们对该处理器进行了FPGA原型设计，并成功完成了功能验证。在原型PIM处理器上运行具有不同工作负载需求的应用的实验结果表明，所提出的技术可实现高达29.54%的节能。
