Uploaded October 2022 | Updated September 2026, 1 week ago
Deep convolutional neural networks (CNNs) have achieved remarkable success in computer vision tasks, like image classification. This is the result of the availability of a large amount of training samples, as well as being able to leverage huge computational and memory resources.
This, however, poses challenges for their applicability to standalone smart agents deployed in new and dynamic environments. In these cases, there is a need for agents to continually learn about novel classes they encounter from very few training samples without forgetting previous knowledge of old classes. At the same time, these agents need to learn efficiently even when computing resources are extremely limited. Our research aims to find solutions to these problems via the physics of in-memory computing, and mathematical models that can efficiently run on in-memory computing hardware.
arxiv.org/abs/2207.06810
Deep convolutional neural networks (CNNs) have achieved remarkable success in computer vision tasks, like image classification. This is the result of the availability of a large amount of training samples, as well as being able to leverage huge computational and memory resources.
This, however, poses challenges for their applicability to standalone smart agents deployed in new and dynamic environments. In these cases, there is a need for agents to continually learn about novel classes they encounter from very few training samples without forgetting previous knowledge of old classes. At the same time, these agents need to learn efficiently even when computing resources are extremely limited. Our research aims to find solutions to these problems via the physics of in-memory computing, and mathematical models that can efficiently run on in-memory computing hardware.
arxiv.org/abs/2207.06810
