Convolutional neural networks are now seeing widespread use in a variety of fields,
including image classification, facial and object recognition, medical imaging analysis, and …

Although convolutional neural networks (CNNs) have made significant progress, their
deployment onboard is still challenging because of their complexity and high processing …

Abstract Recently, Stitchable Neural Networks (SN-Net) is proposed to stitch some pre-
trained networks for quickly building numerous networks with different complexity and …

Deep neural networks (DNNs) have achieved great success in many data processing
applications. However, high computational complexity and storage cost make deep learning …

Existing knowledge distillation (KD) methods are mainly based on features, logic, or
attention, where features and logic represent the results of reasoning at different stages of a …

While large transformer-based vision models have achieved remarkable performance on a
variety of Computer Vision (CV) applications, they are cumbersome to fine-tune for target …

Hyperdimensional computing (HDC) is emerging as an efficient and robust computing
paradigm that has strong resilience to various types of errors. The robustness of HDC makes …