1,600 research outputs found

    Ingress of threshold voltage-triggered hardware trojan in the modern FPGA fabric–detection methodology and mitigation

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    The ageing phenomenon of negative bias temperature instability (NBTI) continues to challenge the dynamic thermal management of modern FPGAs. Increased transistor density leads to thermal accumulation and propagates higher and non-uniform temperature variations across the FPGA. This aggravates the impact of NBTI on key PMOS transistor parameters such as threshold voltage and drain current. Where it ages the transistors, with a successive reduction in FPGA lifetime and reliability, it also challenges its security. The ingress of threshold voltage-triggered hardware Trojan, a stealthy and malicious electronic circuit, in the modern FPGA, is one such potential threat that could exploit NBTI and severely affect its performance. The development of an effective and efficient countermeasure against it is, therefore, highly critical. Accordingly, we present a comprehensive FPGA security scheme, comprising novel elements of hardware Trojan infection, detection, and mitigation, to protect FPGA applications against the hardware Trojan. Built around the threat model of a naval warship’s integrated self-protection system (ISPS), we propose a threshold voltage-triggered hardware Trojan that operates in a threshold voltage region of 0.45V to 0.998V, consuming ultra-low power (10.5nW), and remaining stealthy with an area overhead as low as 1.5% for a 28 nm technology node. The hardware Trojan detection sub-scheme provides a unique lightweight threshold voltage-aware sensor with a detection sensitivity of 0.251mV/nA. With fixed and dynamic ring oscillator-based sensor segments, the precise measurement of frequency and delay variations in response to shifts in the threshold voltage of a PMOS transistor is also proposed. Finally, the FPGA security scheme is reinforced with an online transistor dynamic scaling (OTDS) to mitigate the impact of hardware Trojan through run-time tolerant circuitry capable of identifying critical gates with worst-case drain current degradation

    Cross-Layer Approaches for an Aging-Aware Design of Nanoscale Microprocessors

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    Thanks to aggressive scaling of transistor dimensions, computers have revolutionized our life. However, the increasing unreliability of devices fabricated in nanoscale technologies emerged as a major threat for the future success of computers. In particular, accelerated transistor aging is of great importance, as it reduces the lifetime of digital systems. This thesis addresses this challenge by proposing new methods to model, analyze and mitigate aging at microarchitecture-level and above

    Low-Cost Throttle-By-Wire-System Architecture For Two-Wheeler Vehicles

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    This paper investigates the performance of a low-cost Throttle-by-Wire-System (TbWS) for two-wheeler applications. Its consisting of an AMR throttle position sensor and a position controlled stepper motor driven throttle valve actuator. The decentralized throttle position sensor is operating contactless and acquires redundant data. Throttle valve actuation is realized through a position controlled stepper motor, sensing its position feedback by Hall effect. Using a PI-controller the stepper motors position is precisely set. Sensor and actuator units are transceiving data by a CAN bus. Furthermore, failsafe functions, plausibility checks, calibration algorithms and energy saving modes have been implemented. Both modules have been evaluated within a Hardware-in-the-Loop test environment in terms of reliability and measurement/positioning performance before the TbWS was integrated in a Peugeot Kisbee 50 4T (Euro 5/injected). Finally, the sensor unit comes with a measurement deviation of less then 0.16% whereas the actuator unit can approach throttle valve positions with a deviation of less then 0.37%. The actuators settling time does not exceed 0.13s while stable, step-loss free and noiseless operation

    Robust elbow angle prediction with aging soft sensors via output-level domain adaptation

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    Wearable devices equipped with soft sensors provide a promising solution for body movement monitoring. Specifically, body movements like elbow flexion can be captured by monitoring the stretched soft sensors’ resistance changes. However, in addition to stretching, the resistance of a soft sensor is also influenced by its aging, which makes the resistance a less stable indicator of the elbow angle. In this paper, we leverage the recent progress in Deep Learning and address the aforementioned issue by formulating the aging-invariant prediction of elbow angles as a domain adaption problem. Specifically, we define the soft sensor data (i.e., resistance values) collected at different aging levels as different domains and adapt a regression neural network among them to learn domain-invariant features. However, unlike the popular pairwise domain adaptation problem that only involves one source and one target domain, ours is more challenging as it has “infinite” target domains due to the non-stop aging. To address this challenge, we novelly propose an output-level domain adaptation approach which builds on the fact that the elbow angles are in a fixed range regardless of aging. Experimental results show that our method enables robust and accurate prediction of elbow angles with aging soft sensors, which significantly outperforms supervised learning ones that fail to generalize to aged sensor data
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