A System-Level Simulation Model for a Protocol Processor

As the development of integrated circuit technology continues to follow Moore’s law the complexity of circuits increases exponentially. Traditional hardware description languages such as VHDL and Verilog are no longer powerful enough to cope with this level of complexity and do not provide facilities for hardware/software codesign. Languages such as SystemC are intended to solve these problems by combining the powerful expression of high level programming languages and hardware oriented facilities of hardware description languages. To fully replace older languages in the desing flow of digital systems SystemC should also be synthesizable. The devices required by modern high speed networks often share the same tight constraints for e.g. size, power consumption and price with embedded systems but have also very demanding real time and quality of service requirements that are difficult to satisfy with general purpose processors. Dedicated hardware blocks of an application specific instruction set processor are one way to combine fast processing speed, energy efficiency, flexibility and relatively low time-to-market. Common features can be identified in the network processing domain making it possible to develop specialized but configurable processor architectures. One such architecture is the TACO which is based on transport triggered architecture. The architecture offers a high degree of parallelism and modularity and greatly simplified instruction decoding. For this M.Sc.(Tech) thesis, a simulation environment for the TACO architecture was developed with SystemC 2.2 using an old version written with SystemC 1.0 as a starting point. The environment enables rapid design space exploration by providing facilities for hw/sw codesign and simulation and an extendable library of automatically configured reusable hardware blocks. Other topics that are covered are the differences between SystemC 1.0 and 2.2 from the viewpoint of hardware modeling, and compilation of a SystemC model into synthesizable VHDL with Celoxica Agility SystemC Compiler. A simulation model for a processor for TCP/IP packet validation was designed and tested as a test case for the environment.

Free Prostate-specific Antigen Forms and Kallikrein-related Peptidase 2: Tools for Prostate Cancer Diagnostics

Prostate-specific antigen (PSA) is a marker that is commonly used in estimating prostate cancer risk. Prostate cancer is usually a slowly progressing disease, which might not cause any symptoms whatsoever. Nevertheless, some cases of cancer are aggressive and need to be treated before they become life-threatening. However, the blood PSA concentration may rise also in benign prostate diseases and using a single total PSA (tPSA) measurement to guide the decision on further examinations leads to many unnecessary biopsies, over-detection, and overtreatment of indolent cancers which would not require treatment. Therefore, there is a need for markers that would better separate cancer from benign disorders, and would also predict cancer aggressiveness. The aim of this study was to evaluate whether intact and nicked forms of free PSA (fPSA-I and fPSA-N) or human kallikrein-related peptidase 2 (hK2) could serve as new tools in estimating prostate cancer risk. First, the immunoassays for fPSA-I and free and total hK2 were optimized so that they would be less prone to assay interference caused by interfering factors present in some blood samples. The optimized assays were shown to work well and were used to study the marker concentrations in the clinical sample panels. The marker levels were measured from preoperative blood samples of prostate cancer patients scheduled for radical prostatectomy. The association of the markers with the cancer stage and grade was studied. It was found that among all tested markers and their combinations especially the ratio of fPSA-N to tPSA and ratio of free PSA (fPSA) to tPSA were associated with both cancer stage and grade. They might be useful in predicting the cancer aggressiveness, but further follow-up studies are necessary to fully evaluate the significance of the markers in this clinical setting. The markers tPSA, fPSA, fPSA-I and hK2 were combined in a statistical model which was previously shown to be able to reduce unnecessary biopsies when applied to large screening cohorts of men with elevated tPSA. The discriminative accuracy of this model was compared to models based on established clinical predictors in reference to biopsy outcome. The kallikrein model and the calculated fPSA-N concentrations (fPSA minus fPSA-I) correlated with the prostate volume and the model, when compared to the clinical models, predicted prostate cancer in biopsy equally well. Hence, the measurement of kallikreins in a blood sample could be used to replace the volume measurement which is time-consuming, needs instrumentation and skilled personnel and is an uncomfortable procedure. Overall, the model could simplify the estimation of prostate cancer risk. Finally, as the fPSA-N seems to be an interesting new marker, a direct immunoassay for measuring fPSA-N concentrations was developed. The analytical performance was acceptable, but the rather complicated assay protocol needs to be improved until it can be used for measuring large sample panels.