Ultra-high throughput string matching for deep packet inspection

Deep Packet Inspection (DPI) involves searching a packet's header and payload against thousands of rules to detect possible attacks. The increase in Internet usage and growing number of attacks which must be searched for has meant hardware acceleration has become essential in the prevention of DPI becoming a bottleneck to a network if used on an edge or core router. In this paper we present a new multi-pattern matching algorithm which can search for the fixed strings contained within these rules at a guaranteed rate of one character per cycle independent of the number of strings or their length. Our algorithm is based on the Aho-Corasick string matching algorithm with our modifications resulting in a memory reduction of over 98% on the strings tested from the Snort ruleset. This allows the search structures needed for matching thousands of strings to be small enough to fit in the on-chip memory of an FPGA. Combined with a simple architecture for hardware, this leads to high throughput and low power consumption. Our hardware implementation uses multiple string matching engines working in parallel to search through packets. It can achieve a throughput of over 40 Gbps (OC-768) when implemented on a Stratix 3 FPGA and over 10 Gbps (OC-192) when implemented on the lower power Cyclone 3 FPGA

Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device

Currently, most designers face a daunting task to research different design flows and learn the intricacies of specific software from various manufacturers in hardware/software co-design. An urgent need of creating a scalable hardware/software co-design platform has become a key strategic element for developing hardware/software integrated systems. In this paper, we propose a new design flow for building a scalable co-design platform on FPGA-based system-on-chip. We employ an integrated approach to implement a histogram oriented gradients (HOG) and a support vector machine (SVM) classification on a programmable device for pedestrian tracking. Not only was hardware resource analysis reported, but the precision and success rates of pedestrian tracking on nine open access image data sets are also analysed. Finally, our proposed design flow can be used for any real-time image processingrelated products on programmable ZYNQ-based embedded systems, which benefits from a reduced design time and provide a scalable solution for embedded image processing products

FPGA-based operational concept and payload data processing for the Flying Laptop satellite

Flying Laptop is the first small satellite developed by the Institute of Space Systems at the Universität Stuttgart. It is a test bed for an on-board computer with a reconfigurable, redundant and self-controlling high computational ability based on the field pro- grammable gate arrays (FPGAs). This Technical Note presents the operational concept and the on-board payload data processing of the satellite. The designed operational concept of Flying Laptop enables the achievement of mission goals such as technical demonstration, scientific Earth observation, and the payload data processing methods. All these capabilities expand its scientific usage and enable new possibilities for real-time applications. Its hierarchical architecture of the operational modes of subsys- tems and modules are developed in a state-machine diagram and tested by means of MathWorks Simulink-/Stateflow Toolbox. Furthermore, the concept of the on-board payload data processing and its implementation and possible applications are described

An FPGA-based infant monitoring system

We have designed an automated visual surveillance system for monitoring sleeping infants. The low-level image processing is implemented on an embedded Xilinx’s Virtex II XC2v6000 FPGA and quantifies the level of scene activity using a specially designed background subtraction algorithm. We present our algorithm and show how we have optimised it for this platform

An FPGA implementation of pattern-Selective pyramidal image fusion

The aim of image fusion is to combine multiple images (from one or more sensors) into a single composite image that retains all useful data without introducing artefacts. Pattern-selective techniques attempt to identify and extract whole features in the source images to use in the composite. These techniques usually rely on multiresolution image representations such as Gaussian pyramids, which are localised in both the spatial and spatial-frequency domains, since they enable identification of features at many scales simultaneously. This paper presents an FPGA implementation of pyramidal decomposition and subsequent fusion of dual video streams. This is the first reported instance of a hardware implementation of pattern-selective pyramidal image fusion. Use of FPGA technology has enabled a design that can fuse dual video streams (greyscale VGA, 30fps) in real-time, and provides approximately 100 times speedup over a 2.8GHz Pentium-