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The design of specialized hardware for Network Intrusion Detection has been subject of intense research over the last decade due to its considerably higher performance compared to software implementations. In this context, one of the limiting factors is the finite amount of memory resources versus the increasing number of threat patterns to be analyzed. This paper proposes an architecture based on the Huffman algorithm for encoding, storage and decoding of these patterns in order to optimize such resources. We have made tests with simulation and synthesis in FPGA of rule subsets of the Snort software, and analysis indicate a saving of up to 73 percent of the embedded memory resources of the chip.

In this work, a system level design and conception of a System-on-a-Chip (SoC) for the execution of cognitive agents in robotics will be presented. The cognitive model of the Concurrent Autonomous Agent (CAA), which was already successfully applied in several robotics applications, is used as a reference for the development of the hardware architecture. This cognitive model comprises three levels that run concurrently, namely the reactive level (perception-action cycle that executes predefined behaviours), the instinctive level (receives goals from cognitive level and uses a knowledge based system for selecting behaviours in the reactive level) and the cognitive level (planning). For the development of such system level hardware model, the C++ library SystemC with Transaction Level Modelling (TLM) 2.0 will be used. A system model of a module that executes a knowledge based system is presented, followed by a system level description of a processor dedicated to the execution of the Graphplan planning algorithm. The buses interconnecting these modules are modelled by the TLM generic payload. Results from simulated experiments with complex knowledge bases for solving planning problems in different robotics contexts demonstrate the correctness of the proposed architecture. Finally, a discussion on performance gains takes place in the end.

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