In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
This work addresses the challenge of efficiently storing and retrieving data for continual learning in AI systems, with a focus on hardware implementation, though it is incremental as it builds on existing memory-based architectures.
The paper tackles the problem of few-shot continual learning by proposing an explicit memory unit that uses in-memory compute cores based on phase-change memory to physically superpose training examples and expand for new classes, achieving classification accuracy within 1.28%--2.5% of a state-of-the-art software baseline on CIFAR-100 and miniImageNet datasets.
Continually learning new classes from a few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage, in which new examples and classes can be incrementally stored and efficiently retrieved. One viable architectural solution is to tightly couple a stationary deep neural network to a dynamically evolving explicit memory (EM). As the centerpiece of this architecture, we propose an EM unit that leverages energy-efficient in-memory compute (IMC) cores during the course of continual learning operations. We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM). Specifically, the physical superposition of a few encoded training examples is realized via in-situ progressive crystallization of PCM devices. The classification accuracy achieved on the IMC core remains within a range of 1.28%--2.5% compared to that of the state-of-the-art full-precision baseline software model on both the CIFAR-100 and miniImageNet datasets when continually learning 40 novel classes (from only five examples per class) on top of 60 old classes.