Hardware/Software Co-Design of a Traffic Sign Recognition System Using Zynq FPGAs

Han, Yingjie; Virupakshappa, Kushal; Pinto, Esdras Vitor Silva; Oruklu, Erdal

doi:10.3390/electronics4041062

Cited by 18 publications

(15 citation statements)

References 25 publications

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“…10. K-nearest neighbour (KNN): a simple, nonparametric classifier in which the class of each object is determined with respect to the classes assigned to the nearest k objects, that is, it is specified according to the most repeated labels of these k objects ( Han et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Disentangling diatom species complexes: does morphometry suffice?

Blanco¹,

Borrego-Ramos²,

Olenici

2017

PeerJ

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Accurate taxonomic resolution in light microscopy analyses of microalgae is essential to achieve high quality, comparable results in both floristic analyses and biomonitoring studies. A number of closely related diatom taxa have been detected to date co-occurring within benthic diatom assemblages, sharing many morphological, morphometrical and ecological characteristics. In this contribution, we analysed the hypothesis that, where a large sample size (number of individuals) is available, common morphometrical parameters (valve length, width and stria density) are sufficient to achieve a correct identification to the species level. We focused on some common diatom taxa belonging to the genus Gomphonema. More than 400 valves and frustules were photographed in valve view and measured using Fiji software. Several statistical tools (mixture and discriminant analysis, k-means clustering, classification trees, etc.) were explored to test whether mere morphometry, independently of other valve features, leads to correct identifications, when compared to identifications made by experts. In view of the results obtained, morphometry-based determination in diatom taxonomy is discouraged.

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Section: Methodsmentioning

confidence: 99%

Disentangling diatom species complexes: does morphometry suffice?

Blanco¹,

Borrego-Ramos²,

Olenici

2017

PeerJ

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“…The applications of FPGAs are constantly increasing, specifically, in the field of networking, security, and artificial intelligence [1,19,20], but modern FPGAs lack a soft core for CAM which is an essential element in searching-based applications. Thus, there is a need to develop an optimal CAM core, which can be used for packet classification in modern re-configurable networking systems on FPGAs.…”

Section: Motivationsmentioning

confidence: 99%

“…Sw is forwarded to LB when the IF condition is false. Eight matchlines (M_L [1], M_L [1], ..., M_L [7]) are created by comparing the 4 LSBs of Sw with the output of 4 LUTRAMs in each of the eight rows of LUTs. The comparison is shown using XNOR gates in line #7 of Algorithm 1.…”

Section: Searching Operationmentioning

confidence: 99%

“…Field-programmable gate arrays (FPGAs) are becoming an increasingly favorable platform for systems implementation because of their hardware-like performance and software-like reconfigurability. Modern FPGAs provide a vast amount of configurable logic and embedded memory blocks operating at a high clock rate [1,2]. For example, the 16-nm Xilinx UltraSCALE FPGA and 14-nm Intel Stratix FPGA provide a massive amount of memory and logic resources which can be configured on-the-fly to implement the given functionality.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Zi-CAM: A Power and Resource Efficient Binary Content-Addressable Memory on FPGAs

2019

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Content-addressable memory (CAM) is a type of associative memory, which returns the address of a given search input in one clock cycle. Many designs are available to emulate the CAM functionality inside the re-configurable hardware, field-programmable gate arrays (FPGAs), using static random-access memory (SRAM) and flip-flops. FPGA-based CAMs are becoming popular due to the rapid growth in software defined networks (SDNs), which uses CAM for packet classification. Emulated designs of CAM consume much dynamic power owing to a high amount of switching activity and computation involved in finding the address of the search key. In this paper, we present a power and resource efficient binary CAM architecture, Zi-CAM, which consumes less power and uses fewer resources than the available architectures of SRAM-based CAM on FPGAs. Zi-CAM consists of two main blocks. RAM block (RB) is activated when there is a sequence of repeating zeros in the input search word; otherwise, lookup tables (LUT) block (LB) is activated. Zi-CAM is implemented on Xilinx Virtex-6 FPGA for the size 64 × 36 which improved power consumption and hardware cost by 30 and 32%, respectively, compared to the available FPGA-based CAMs.

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“…Support vector machine (SVM) and neural networks (NNs) are popular for use as classifiers. In [2], a new TSR algorithm flow is proposed, which performs exceptionally robustly against environmental challenges, such as partially obscured, rotated and skewed traffic signs. Another critical component is the embedded system implementation of the algorithm on a programmable logic device that can enable real-time operation.…”

Section: Traffic Signs Recognitionmentioning

confidence: 99%