Abstract
As the number of edge devices grows to tens of billions, the importance of intelligent computing has been shifted from cloud datacenters to edge devices. On-device training, which enables the personalization of a machine learning (ML) model for each user, is crucial in the success of edge intelligence. However, battery-powered edge devices cannot afford huge computations and memory accesses involved in the training. Processing-in-Memory (PIM) is a promising technology to overcome the memory bandwidth and energy problem by combining processing logic into the memory. Many PIM chips [1]–[5] have accelerated ML inference using analog or digital-based logic with sparsity handling. Two-way transpose PIM [6] supports backpropagation, but it lacks gradient calculation and weight update, required for end-to-end ML training.
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