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The GigaNetIC project aims to develop high-speed components for networking applications based on massively parallel architectures. A central part of this project is the design, evaluation, and realization of a parameterizable network processing unit. In this paper we present a design methodology for network processors which encompasses the research areas from the application software down to the gate level of the chip. Key components of this holistic approach have been successfully applied to characteristic examples of architecture refinements.

Application specific instruction-set processors combine an efficient general purpose core with special purpose functionality that is tailored to a particular application domain. Since the extension of an instruction set and its utilization are non-trivial tasks, sophisticated tools have to provide guidance and support during design. Feedback driven optimization allows for the highest level of specialization, but calls for a simulator that is aware of the newly proposed instructions, a compiler that makes use of these instructions without manual intervention, and an application program that is representative for the targeted application domain.In this paper we introduce an approach for the extension of instruction sets that is built around a concise yet powerful processor abstraction. The specification of a processor is well suited to automatically generate the important parts of a compiler backend and cycle-accurate simulator. A typical design cycle involves the execution of the representative application program, evaluation of performance statistics collected by the simulator, refinement of the processor specification guided by performance statistics, and update of the compiler and simulator according to the refined specification. We demonstrate the usefulness of our novel approach by example of an instruction set for symmetric ciphers.

Introduction: Iron-based nanocatalysts are known as a new generation heterogeneous Fenton catalyst, replacing the traditional Fenton catalyst system which has many disadvantages in experimental processes and industrial applications. In this study, we focused on the preparation of iron nanoparticles and their use when embedded in traditional supports, as well as tested their catalytic activity by modified Fenton-type oxidation of methylene blue (MB) substrate. Method: Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), X-ray diffraction (XRD) and UV-vis were used for physio-chemical characterization of the catalysts. Results: Iron nanoparticles were obtained in the reduction of iron salt by sodium borohydride (NaBH4), with particle size in the range of 4-5 nm. Fe-X (X represents C, Bentonite, Al2O3, or ZnO) was synthesized in high yield and applied to the Fenton oxidation of MB; approximately 99% conversion was observed in the case of Fe-C. Conclusion: Supported iron nanoparticles are active catalysts for the oxidation of MB; however, there are limitations if pH is above 3.

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