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The rapid advancements in artificial intelligence (AI), particularly the Large Language Models (LLMs), have profoundly affected our daily work and communication forms. However, it is still a challenge to deploy LLMs on resource-constrained…

As the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications,…

This paper introduces a closed-form least-squares (LS) design approach for fast-convolution (FC) based variable-bandwidth (VBW) finite-impulse-response (FIR) filters. The proposed LS design utilizes frequency sampling and the VBW filter…

Here we present an in-depth study of the behaviour of the Fast Folding Algorithm, an alternative pulsar searching technique to the Fast Fourier Transform. Weaknesses in the Fast Fourier Transform, including a susceptibility to red noise,…

Convolutional Neural Networks (CNNs) have gained widespread popularity in the field of computer vision and image processing. Due to huge computational requirements of CNNs, dedicated hardware-based implementations are being explored to…

The main role of the ITER Radial Neutron Camera (RNC) diagnostic is to measure in real-time the plasma neutron emissivity profile at high peak count rates for a time duration up to 500 s. Due to the unprecedented high performance conditions…

Bitmap index is recognized as a promising candidate for online analytics processing systems, because it effectively supports not only parallel processing but also complex and multi-dimensional queries. However, bitmap index creation is a…

The core architecture, tests in the lab and first results of a Fast Fourier Transform (FFT) spectrometer are described. It is based on a commercially available fast digital sampler (AC240) with an on-board Field Programmable Gate Array…

The effective use of computer vision and machine learning for on-orbit applications has been hampered by limited computing capabilities, and therefore limited performance. While embedded systems utilizing ARM processors have been shown to…

Convolutional neural network (CNN) dataflow inference accelerators implemented in Field Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower latency compared to CNN execution on CPUs or GPUs. However, the…

Array-based, direct-sampling radio telescopes have computational and communication requirements unsuited to conventional computer and cluster architectures. Synchronization must be strictly maintained across a large number of parallel data…

While neural network quantization effectively reduces the cost of matrix multiplications, aggressive quantization can expose non-matrix-multiply operations as significant performance and resource bottlenecks on embedded systems. Addressing…

Cyclostationary analysis is widely used in signal processing, particularly in the analysis of human-made signals, and spectral correlation density (SCD) is often used to characterise cyclostationarity. Unfortunately, for real-time…

Spiking neural networks (SNNs) have been widely used due to their strong biological interpretability and high energy efficiency. With the introduction of the backpropagation algorithm and surrogate gradient, the structure of spiking neural…

Convolutional Neural Networks are extensively used in a wide range of applications, commonly including computer vision tasks like image and video classification, recognition, and segmentation. Recent research results demonstrate that…

Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploited. However, their extensive use is restricted by their high…

Fractional programming (FP) arises in various communications and signal processing problems because several key quantities in the field are fractionally structured, e.g., the Cram\'{e}r-Rao bound, the Fisher information, and the…

This paper presents Systolic-CNN, an OpenCL-defined scalable, run-time-flexible FPGA accelerator architecture, optimized for accelerating the inference of various convolutional neural networks (CNNs) in multi-tenancy cloud/edge computing.…

FPGA-based heterogeneous architectures provide programmers with the ability to customize their hardware accelerators for flexible acceleration of many workloads. Nonetheless, such advantages come at the cost of sacrificing programmability.…

The Square Kilometre Array (SKA) will operate one of the world's largest continuous scientific data systems, sustaining petascale imaging under strict power envelopes. Current radio-interferometric pipelines typically achieve only 4-14% of…