Since Google proposed Transformer in 2017, it has made significant natural language
processing (NLP) development. However, the increasing cost is a large amount of …

With the rapid development and continuous evolution of convolutional neural networks
(CNNs), FPGAs have become one of the most attractive candidates for deploying CNNs due …

With the rapid development of the Internet of Things (IoT), it has become a common concern
of academia and industry to provide real-time high performance services for edge-side …

Over the last few years, deep learning on irregular 3D data given its wide range of
applications has become one of the active topics in the field. While field programmable gate …

Convolutional neural networks (CNNs) are widely adopted in safety-critical systems,
including space applications and autonomous vehicles. Field-programmable gate arrays …

Convolutional Neural Networks (CNNs) can benefit from the computational reductions
provided by the Winograd minimal filtering algorithm and weight pruning. However …

Convolutional neural networks (CNNs) are widely utilized in intelligent edge computing
applications such as computational vision and image processing. However, as the number …

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

In multi-tasking scenarios with dynamically changing loads, the parallel computing of
convolutional neural networks (CNNs) causes high energy and resource consumption in the …

Abstract A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely
response for tomato defect detection is essential for factory automation. Because of various …