Post-Silicon validation is playing an increasingly important role as more chips are failing in the functional mode due to either manufacturing defects escaped during scan-based tests or design bugs missed during pre-silicon validation. Critical to the diagnosis engineer is the ability to observe as many relevant internal signal values as possible during debug. To do so, trace buffers have been proposed for enhancing the observability of internal signals during post-silicon debug. Trace Buffers are used to trace (record the values of) the internal signals in real-time when chip is in its normal operation. However, existing trace buffer architectures trace very few signals for a large number of cycles. Thus, even with a good subset of signals traced, one often still cannot restore all the relevant values in the circuit. In this work, we propose two different flexible trace buffer architectures that can restore the values for all signals by making the trace signals configurable. In addition, the buffer space can also be shared among different traced signals which makes the architectures highly flexible. As compared to conventional trace buffer architectures, the new architectures have comparable area overhead but offer the ability to restore all signals in the circuit. For cases of less than 100% restoration, the ability of circuit invariants to improve the signal restoration is explored. A promising direction for the future work is provided where targeted invariants may lead to better restoration scenario during post-silicon validation. / Master of Science

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The master's thesis deals with the restoration of audio signals that have been damaged by clipping. Used methods are based on sparse representations of signals. The introduction of the thesis explains the issue of clipping and mentions the list of already existing methods that solve declipping, which are followed by the thesis. In the next chapter, the necessary theory of sparse representations and the proximal algorithms is described, including specific representatives from the category of convex optimization problems. The thesis contains declipping algorithm implemented in Matlab software environment. Chosen method for solving the task uses the Condat algorithm or Generic proximal algorithm for convex optimization and solves minimization of sum of three convex functions. The result of the thesis is five versions of algorithm and three of them have implemented psychoacoustic model for results improvement. For each version has been found optimal setting of parameters. The restoration quality results are evaluated using objective measurements like SDR and PEMO-Q and also using subjective listening test.