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The emergence of design space exploration (DSE) technology has reduced the cost of searching for pragma configurations that lead to optimal performance microarchitecture. However, obtaining synthesis reports for a single design candidate can be time-...

Despite the economic importance and research relevance of strawberries, advances in agricultural engineering for this crop have been hampered by pervasive occlusion challenges. Accurate fruit counting is crucial for both yield prediction and ...

• Automated occlusion-robust counting for accurate strawberry yield assessment.
• Enhanced strawberry detection with improved YOLOv5s and attention.
• Developed the MultiMap algorithm for precise counting from tracking results.
• ...

Neural architecture search (NAS) automates the design of neural networks for specific tasks. Recently, zero-shot NAS has attracted much attention. Unlike traditional NAS, which relies on training to rank architectures, zero-shot NAS uses gradients ...

As a promising edge intelligence technology, federated learning (FL) enables Internet of Things (IoT) devices to train the models collaboratively while ensuring the data privacy and security. Recently, hierarchical FL (HFL) has been designed to promote ...

The surge in intelligent edge computing has propelled the adoption and expansion of the distributed embedded systems (DESs). Numerous scheduling strategies are introduced to improve the DES throughput, such as latency-aware and group-based hierarchical ...

Block-circulant matrix (BCM) compression has garnered much attention in the hardware acceleration of convolutional neural networks (CNNs) due to its regularity and efficiency. However, constrained by the difficulty of exploring the compression parameter ...

Recently, algorithm-hardware (HW) co-exploration for neural networks (NNs) has become the key to obtaining high-quality solutions. However, previous efforts for field-programmable gate arrays (FPGAs) focus on neural architecture search (NAS) while lacking ...

To address the power management challenges in deep neural networks (DNNs), dynamic voltage and frequency scaling (DVFS) technology is garnering attention for its ability to enhance energy efficiency without modifying the structure of DNNs. However, ...

Attention-based transformers have achieved significant performance breakthroughs in natural language processing (NLP) and computer vision (CV) tasks. Meanwhile, the ever-increasing length of today’s input sequences puts much pressure on computing ...

The proliferation of multimedia applications and increased computing power of mobile devices have led to the development of personalized artificial intelligent (AI) applications that utilize the massive user-information residing on them. However, the ...

Graph random walk sampling is becoming increasingly important with the widespread popularity of graph applications. It aims to capture the desirable graph properties by launching multiple walkers to collect feature paths. However, previous research ...

Research on emergent communication has recently gained significant traction as a promising avenue for the linguistic community to unravel human language's origins and explore artificial intelligence's generalization capabilities. Current research has ...

The cloud–edge collaborative inference requires efficient scheduling of artificial intelligence (AI) tasks to the appropriate edge intelligence devices. Gls DNN inference latency has become a vital basis for improving scheduling efficiency. However,...

This article presents LegoSwap, a cross-device memory swapping mechanism for mobile devices. It exploits the unbalanced utilization of memory resources across devices. With LegoSwap, remote memory is utilized in a seamless plug-and-play manner. It ...

Transportation big data (TBD) are increasingly combined with artificial intelligence to mine novel patterns and information due to the powerful representational capabilities of deep neural networks (DNNs), especially for anti-COVID19 applications. The ...

Research on emergent communication between deep-learning-based agents has received extensive attention due to its inspiration for linguistics and artificial intelligence. However, previous attempts have hovered around emerging communication under ...

In recent years, graph neural networks (GNNs) have achieved impressive performance in various application fields by extracting information from graph-structured data. It contains extensive feature aggregation operations and has become a performance ...

Predictor-based Neural Architecture Search (NAS) offers a promising solution for enhancing the efficiency of traditional NAS methods. However, it is non-trivial to train the predictor with limited architecture evaluations for efficient NAS. While ...

Predictor-based Neural Architecture Search (NAS) offers a promising solution for enhancing the efficiency of traditional NAS methods. However, it is non-trivial to train the predictor with limited architecture evaluations for efficient NAS. In this paper,...

Growing pattern matching applications are employing finite automata as their basic processing model. These applications match tens to thousands of patterns on a large amount of data, which brings a great challenge to conventional processors. Therefore ...