Leveraging ECRAM for Edge Continual Learning

2026-07-21

AI Summary

This work addresses the energy and latency bottlenecks in continual learning on edge devices caused by frequent data movement, as existing in-memory computing solutions struggle to balance accuracy and efficiency due to noise sensitivity and insufficient training support. The paper proposes CLASP, the first end-to-end programmable in-memory computing system for continual learning, built upon back-end-of-line compatible ECRAM devices. Through a synergistic hardware-software co-design and a software-visible assembly-level instruction set, CLASP flexibly supports diverse continual learning algorithms, including rehearsal-free and experience replay methods. Experimental results demonstrate that CLASP achieves near-GPU accuracy on MNIST while delivering a 67× speedup and 132× improvement in energy efficiency compared to conventional architectures.

Abstract

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.

Problem

• Edge Continual Learning
• In-Memory Computing
• ECRAM
• Catastrophic Forgetting
• Energy Efficiency

Innovation

• ECRAM
• In-Memory Computing
• Continual Learning
• Edge AI
• Hardware-Software Co-design