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Convolutional Neural Networks (CNNs) can achieve high classification accuracy while they require complex computation. Binarized Neural Networks (BNNs) with binarized weights and activations can simplify computation but suffer from obvious accuracy loss. In this paper, low bit-width CNNs, BNNs and standard CNNs are compared to show that low bit-width CNNs is better suited for embedded systems. An architecture...
Deformable Part Model (DPM) is one of the best algorithms for image-based object detection. However, the high computation intensity leads to relatively long detecting time. Even with the powerful CPU or GPU computing system, it is still too slow for practical applications. To solve this problem, this paper proposes a high performance DPM accelerator based on FPGA, where a dedicated JPEG decoder is...
This paper describes a high-performance and energy-efficient online video retrieval system called Ameba. Ameba contains a number of custom reconfigurable nodes which are grouped by a novel architecture. The system aims to address the performance and energy efficiency issues for large scale dynamic concurrent query requests. The open SURF approach and a Hamming distance based matching algorithm were...
Algorithms such as Bag of Words and Simhash have been widely used in image recognition. To achieve better performance as well as energy-efficiency, a hardware implementation of these two algorithms is proposed in this paper. To the best of our knowledge, it is the first time that these algorithms have been implemented on hardware for image recognition purpose. The proposed implementation is able to...
In this paper, a high-performance match engine for content-based image retrieval is proposed. Highly customized floating-point(FP) units are designed, to provide the dynamic range and precision of standard FP units, but with considerably less area than standard FP units. Match calculation arrays with various architectures and scales are designed and evaluated. An CBIR system is built on a 12-FPGA...
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