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This paper presents a fully configurable and programmable coprocessor IP core to efficiently compute Artificial Neural Networks (ANNs) in heterogeneous System-on-Chips (SoC). There is an increasing interest in moving applications involving streamed data such as those arising in machine-learning systems (machine-vision, speech-recognition, etc.) to highly-integrated low-power embedded devices. In this...
Hyperspectral imagery is being widely used for accurate object detection and terrain feature classification. Modern imaging spectrometers produce huge amounts of data that are compressed onboard and downloaded to ground stations to be processed. Increasing spectral resolution and data acquisition rates demand more efficient compression techniques to meet downlink bandwidth restrictions. A different...
Singular Value Decomposition (SVD) is a key linear algebraic operation in many scientific and engineering applications, many of them involving high dimensionality datasets and real-time response. In this paper we describe a scalable parallel processing architecture for accelerating the SVD of large m × n matrices. Based on a linear array of simple processing-units (PUs), the proposed architecture...
Singular Value Decomposition (SVD) is a key linear algebraic operation in many scientific and engineering applications. In particular, many computational intelligence systems rely on machine learning methods involving high dimensionality datasets that have to be fast processed for real-time adaptability. In this paper we describe a practical FPGA (Field Programmable Gate Array) implementation of a...
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