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Abstract-Razor is a hybrid technique for dynamic detection and correction of timing errors. A combination of error detecting circuits and micro-architectural recovery mechanisms creates a system that is robust in the face of timing errors, and can be tuned to an efficient operating point by dynamically eliminating unused timing margins. Savings from margin reclamation can be realized as per device power-efficiency improvement, or parametric yield improvement for a batch of devices. In this paper, we apply Razor to a 32 bit ARM processor with a micro-architecture design that has balanced pipeline stages with critical memory access and clock-gating enable paths. The design is fabricated on a UMC 65 nm process, using industry standard EDA tools, with a worst-case STA signoff of 724 MHz. Based on measurements on 87 samples from split-lots, we obtain 52% power reduction for the overall distribution at 1 GHz operation. We present error rate driven dynamic voltage and frequency scaling schemes where runtime adaptation to PVT variations and tolerance of fast transients is demonstrated. All Razor cells are augmented with a sticky error history bit, allowing precise diagnosis of timing errors over the execution of test vectors. We show potential for parametric yield improvement through energy-efficient operation using Razor.Index Terms-Adaptive design, dynamic voltage and frequency scaling, energy-efficient circuits, parametric yield, variation tolerance.

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Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of Mobile Station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an Extended Kalman Filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramér Rao lower bound (PCRLB) is calculated and it is compared with the filters' rootmean-square error performance.

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