TY - GEN AU - Simon-Mertens, Florian ID - 45762 TI - Effizienzanalyse leichtgewichtiger Neuronaler Netze für FPGA-basierte Modulationsklassifikation ER - TY - THES AB - Ever increasing demands on the performance of microchips are leading to ever more complex semiconductor technologies with ever shrinking feature sizes. Complex applications with high demands on safety and reliability, such as autonomous driving, are simultaneously driving the requirements for test and diagnosis of VLSI circuits. Throughout the life cycle of a microchip, uncertainties occur that affect its timing behavior. For example, weak circuit structures, aging effects, or process variations can lead to a change in the timing behavior of the circuit. While these uncertainties do not necessarily lead to a change of the functional behavior, they can lead to a reliability problem. With modular and hybrid compaction two test instruments are presented in this work that can be used for X-tolerant test response compaction in the built-in Faster-than-At-Speed Test (FAST) which is used to detect uncertainties in VLSI circuits. One challenge for test response compaction during FAST is the high and varying X-rate at the outputs of the circuit under test. By dividing the circuit outputs into test groups and separately compacting these test groups using stochastic compactors, the modular compaction is able to handle these high and varying X-rates. To deal with uncertainties on logic interconnects, a method for distinguishing crosstalk and process variation is presented. In current semiconductor technologies, the number of parasitic coupling capacitances between logic interconnects is growing. These coupling capacitances can lead to crosstalk, which causes increased current flow in the logic interconnects, which in turn can lead to increased electromigration. In the presented method, delay maps describing the timing behavior of the circuit outputs at different operating points are used to train artificial neural networks which classify the tested circuits into fault-free and faulty. AU - Sprenger, Alexander ID - 46482 KW - Testantwortkompaktierung KW - Prozessvariation KW - Silicon Lifecycle Management TI - Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen ER - TY - THES AU - Hansmeier, Tim ID - 47837 TI - XCS for Self-awareness in Autonomous Computing Systems ER - TY - THES AB - Wettstreit zwischen der Entwicklung neuer Hardwaretrojaner und entsprechender Gegenmaßnahmen beschreiten Widersacher immer raffiniertere Wege um Schaltungsentwürfe zu infizieren und dabei selbst fortgeschrittene Test- und Verifikationsmethoden zu überlisten. Abgesehen von den konventionellen Methoden um einen Trojaner in eine Schaltung für ein Field-programmable Gate Array (FPGA) einzuschleusen, können auch die Entwurfswerkzeuge heimlich kompromittiert werden um einen Angreifer dabei zu unterstützen einen erfolgreichen Angriff durchzuführen, der zum Beispiel Fehlfunktionen oder ungewollte Informationsabflüsse bewirken kann. Diese Dissertation beschäftigt sich hauptsächlich mit den beiden Blickwinkeln auf Hardwaretrojaner in rekonfigurierbaren Systemen, einerseits der Perspektive des Verteidigers mit einer Methode zur Erkennung von Trojanern auf der Bitstromebene, und andererseits derjenigen des Angreifers mit einer neuartigen Angriffsmethode für FPGA Trojaner. Für die Verteidigung gegen den Trojaner ``Heimtückische LUT'' stellen wir die allererste erfolgreiche Gegenmaßnahme vor, die durch Verifikation mittels Proof-carrying Hardware (PCH) auf der Bitstromebene direkt vor der Konfiguration der Hardware angewendet werden kann, und präsentieren ein vollständiges Schema für den Entwurf und die Verifikation von Schaltungen für iCE40 FPGAs. Für die Gegenseite führen wir einen neuen Angriff ein, welcher bösartiges Routing im eingefügten Trojaner ausnutzt um selbst im fertigen Bitstrom in einem inaktiven Zustand zu verbleiben: Hierdurch kann dieser neuartige Angriff zur Zeit weder von herkömmlichen Test- und Verifikationsmethoden, noch von unserer vorher vorgestellten Verifikation auf der Bitstromebene entdeckt werden. AU - Ahmed, Qazi Arbab ID - 29769 KW - FPGA Security KW - Hardware Trojans KW - Bitstream-level Trojans KW - Bitstream Verification TI - Hardware Trojans in Reconfigurable Computing ER - TY - THES AU - Witschen, Linus Matthias ID - 34041 TI - Frameworks and Methodologies for Search-based Approximate Logic Synthesis ER - TY - GEN AU - Mehlich, Florian ID - 42839 TI - An Evaluation of XCS on the OpenAI Gym ER - TY - GEN AU - Tcheussi Ngayap, Vanessa Ingrid ID - 45715 TI - FreeRTOS on a MicroBlaze Soft-Core Processor with Hardware Accelerators ER - TY - THES AB - Previous research in proof-carrying hardware has established the feasibility and utility of the approach, and provided a concrete solution for employing it for the certification of functional equivalence checking against a specification, but fell short in connecting it to state-of-the-art formal verification insights, methods and tools. Due to the immense complexity of modern circuits, and verification challenges such as the state explosion problem for sequential circuits, this restriction of readily-available verification solutions severely limited the applicability of the approach in wider contexts. This thesis closes the gap between the PCH approach and current advances in formal hardware verification, provides methods and tools to express and certify a wide range of circuit properties, both functional and non-functional, and presents for the first time prototypes in which circuits that are implemented on actual reconfigurable hardware are verified with PCH methods. Using these results, designers can now apply PCH to establish trust in more complex circuits, by using more diverse properties which they can express using modern, efficient property specification techniques. AU - Wiersema, Tobias ID - 26746 KW - Proof-Carrying Hardware KW - Formal Verification KW - Sequential Circuits KW - Non-Functional Properties KW - Functional Properties TI - Guaranteeing Properties of Reconfigurable Hardware Circuits with Proof-Carrying Hardware ER - TY - GEN AB - Automation becomes a vital part in the High-Performance computing system in situational dynamics to take the decisions on the fly. Heterogeneous compute nodes consist of computing resources such as CPU, GPU and FPGA and are the important components of the high-performance computing system that can adapt the automation to achieve the given goal. While implanting automation in the computing resources, management of the resources is one of the essential aspects that need to be taken care of. Tasks are continuously executed on the resources using its unique characteristics. Effective scheduling is essential to make the best use of the characteristics provided by each resource. Scheduling enables the execution of each task by allocating resources so that they take advantage of all the characteristics of the compute resources. Various scheduling heuristics can be used to create effective scheduling, which might require the execution time to schedule the task efficiently. Providing actual execution time is not possible in many cases; hence we can provide the estimations for the actual execution time . The purpose of this master's thesis is to design a predictive model or system that estimates the execution time required to execute tasks using historical execution time data on the heterogeneous compute nodes. In this thesis, regression techniques(SGD Regressor, Passive-Aggressive Regressor, MLP Regressor, and XCSF Regressor) are compared in terms of their prediction accuracy in order to determine which technique produces reliable predictions for the execution time. These estimations must be generated in an online learning environment in which data points arrive in any sequence, one by one, and the regression model must learn from them. After evaluating the regression algorithms, it is seen that the XCSF regressor provides the highest overall prediction accuracy for the supplied data sets. The regression technique's parameters also play a significant role in achieving an acceptable prediction accuracy. As a remark, when using online learning in regression analysis, the accuracy depends upon both the order of sequential data points that are coming to train the model and the parameter configuration for each regression technique. AU - Kashikar, Chinmay ID - 29151 TI - A Comparison of Machine Learning Techniques for the On-line Characterization of Tasks Executed on Heterogeneous Compute Nodes ER - TY - GEN AU - Rehnen, Jakob Werner ID - 22216 TI - Decomposition of Arithmetic Components for the Approximate Circuit Synthesis with EvoApproxLib ER -