Abstract: The efficient utilization of heterogeneous computing systems is crucial for scientists and industrial organizations to execute computationally intensive applications. Task-based programming ...

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Abstract: Parallel interleaved three-level (3L) inverters (PITIs) fed flywheel energy storage systems (FESSs) enable efficient energy conversion for high-power applications. However, PITIs suffer from ...

Abstract: This research introduces a novel Processing Engine (PE) for convolution layers in Deep Neural Network (DNN) accelerators. The PE integrates multi-precision dynamic fraction fixed-point ...

Abstract: Floating-point (FP) multipliers are critical components in high-performance computing systems, particularly in signal processing, graphics, and machine learning applications. However, ...

Abstract: In this article, we extend the CUDAMPILIB framework, which facilitates the programming of parallel applications for multi-node systems with one or more graphical processing units (GPUs) per ...

Abstract: The Multiply and Accumulator (MAC) in Convolution Neural Network (CNN) for image applications demands an efficient matrix multiplier. This study presents an area- and power-efficient ...

Abstract: The importance of grid impedance in the stability analysis of grid-tied converters is widely acknowledged and studied. However, in meshed distribution grids, it becomes crucial to ...

Abstract: This paper proposes a fault tolerant differential power processing (DPP) scheme for parallel-connection photovoltaic (PV) modules. The proposed DPP utilizes two switches to achieve voltage ...

Abstract: In-network computing leverages computational capabilities of network nodes themselves to enable real-time data processing along the transmission path, further shortening the distance between ...

A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor processing by enabling a single light source to perform multiple operations ...

Abstract: Low-light image augmentation is crucial in many applications where visibility is regularly impeded by suboptimal lighting, such as autonomous driving, surveillance, and medical imaging.