Rolling bearing is a key component of rotating machinery, and its fault diagnosis technology
is very important to ensure the safety of equipment. With the rapid development of deep …

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging
capabilities, making it invaluable for ship target recognition. Despite the advancements in …

Modern hardware architectures for Convolutional Neural Networks (CNNs), other than
targeting high performance, aim at dissipating limited energy. Reducing the data movement …

Leveraging approximate multipliers in approximate neural networks (ApproxNNs) can
effectively reduce hardware area and power consumption, making them suitable for edge …

Fine-grained sparse convolutional neural networks (CNNs) achieve a better trade-off
between model accuracy and size than coarse-grained sparse CNNs. Due to irregular data …

Sparse convolutional neural networks (SCNNs) which can prune trivial parameters in the
network while maintaining the model accuracy has been proved to be an attractive approach …

Edge-AI applications face huge challenges in resource-constrained environments,
particularly in enhancing computational efficiency within bandwidth limitations. This work …

Modern hardware architectures for Convolutional Neural Networks (CNNs), other than
targeting high performance, aim at dissipating limited energy. Reducing the data movement …

The rapid adaptation of data driven AI models, such as deep learning inference, training,
Vision Transformers (ViTs), and other HPC applications, drives a strong need for runtime …

The low power and latency of hyperdimensional associative memory (HAM) promotes
hyperdimensional computing (HDC)'s efficiency. However, overheads of HAM can be hardly …