利用电阻式随机存取存储器实现边缘连续学习
Leveraging ECRAM for Edge Continual Learning
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中文总结 AI 辅助
研究自动驾驶等边缘计算平台连续学习问题，提出CLASP系统，其软硬件协同设计，围绕自制的BEOL兼容ECRAM设备，能克服IMC训练挑战，接近GPU内训练精度，实现显著加速和节能。
中文摘要 AI 辅助
一些边缘计算平台，如自动驾驶车辆和智能传感设备，需要通过从现场新数据学习来实时适应动态环境。连续学习已成为边缘训练的一种有前景的解决方案，它结合了成功将先前训练数据的高度概括版本（以避免灾难性遗忘）与最近感测数据相结合的技术。然而，与其他机器学习算法一样，连续学习在通用CPU/GPU和内存之间产生大量数据移动，影响其在边缘平台的适用性。内存计算（IMC）可以减少这种浪费并使连续学习在边缘可行，但它面临两个独特挑战：（1）IMC架构使用有噪声的计算操作，严重损害训练精度；（2）IMC架构对资源高效训练的支持不佳且往往不完整。为应对这些挑战，我们提出了CLASP（连续学习加速系统平台），据我们所知，它是第一个具有IMC加速功能的端到端连续学习系统。CLASP的硬件和软件协同设计，通过软件可见的汇编级指令支持广泛的连续学习算法，这些指令可以无约束地纳入基于机器学习的算法中。CLASP围绕我们制造的与后端（BEOL）兼容的电阻式随机存取存储器（ECRAM）设备设计，该设备可以克服使用其他新兴存储设备进行基于IMC训练的挑战。我们表明，使用ECRAM的CLASP接近GPU内训练的精度，同时在使用MNIST进行无遗忘学习和经验回放时，实现了67倍的加速和132倍的节能。
英文摘要
Several edge computing platforms, such as autonomous vehicles and smart sensing devices, need to adapt to dynamic environments in real time by learning from new data in the field. Continual learning has emerged as a promising solution for edge training, by incorporating techniques that successfully combine a highly summarized version of previously trained data (to avoid catastrophic forgetting) with recently sensed data. However, as is the case with other ML algorithms, continual learning generates significant data movement between general-purpose CPUs/GPUs and memory, impacting the suitability of continual learning for edge platforms. In-memory computing (IMC; also known as processing-using-memory) can curtail this waste and make continual learning feasible at the edge, but it faces two unique challenges: (1) IMC architectures make use of noisy computation operations that significantly harm training accuracy; and (2) IMC architectures have poor and often incomplete support for resource-efficient training. To address these challenges, we propose CLASP (the Continual Learning Acceleration System Platform), which to our knowledge is the first end-to-end system with IMC acceleration for continual learning. The hardware and software of CLASP are co-designed to support a wide range of continual learning algorithms, through software-visible assembly-level instructions that can be incorporated without constraints into ML-based algorithms. CLASP is designed around a back-end-of-line (BEOL) compatible ECRAM device that we fabricate, which can overcome the challenges of IMC-based training using other emerging memory devices. We show that CLASP with ECRAM approaches the accuracy of in-GPU training, while delivering a speedup of 67x and energy savings of 132x for learning without forgetting and experience replay using MNIST.
发表机构
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
