Due to the separate memory and computation units in traditional Von-Neumann architecture,
massive data transfer dominates the overall computing system's power and latency, known …

Leveraging the ReRAM crossbar-based In-Memory-Computing (IMC) to accelerate single
task DNN inference has been widely studied. However, using the ReRAM crossbar for …

Continual Learning (CL) allows applications such as user personalization and household
robots to learn on the fly and adapt to context. This is an important feature when context …

As AI applications become pervasive on edge device, incrementally learning new tasks is
demanded for deep neural network (DNN) models. In this article, we proposed AILC, a …

Artificial neural networks have acquired remarkable achievements in the field of artificial
intelligence. However, it suffers from catastrophic forgetting when dealing with continual …

On-device learning remains a formidable challenge, especially when dealing with resource-
constrained devices that have limited computational capabilities. This challenge is primarily …

With many devices deployed at the extreme edge in dynamic environments the ability to
learn continually on the device is a fast-emerging trend for ultra-low-power Microcontrollers …

In the last few years, research and development on Deep Learning models & techniques for
ultra-low-power devices–in a word, TinyML–has mainly focused on a train-then-deploy …

On-device continual learning (CL) requires the co-optimization of model accuracy and
resource efficiency to be practical. This is extremely challenging because it must preserve …

Continually learning new classes from few training examples without forgetting previous old
classes demands a flexible architecture with an inevitably growing portion of storage, in …