Emerging portable applications, including Human Activity Recognition and Robot Navigation, require always-on sensing technologies to continuously monitor the environment. Continuous sensing, however, strongly dominates the hardware devices' power consumption and consequently hampers the systems' always-on functionality. In this paper we propose a circuitaware Machine Learning scheme that exploits the devices' ability to dynamically tune the quality of its sensors to trade-off system level accuracy versus total system-level power consumption. To this end, we 1) analytically derive the power-quality trade-off space in sensory front-ends; 2) use these equations to make the probabilistic relations between sensory features and their degraded versions explicit in a Bayesian Network classifier; and 3) propose a methodology building upon this model to control the required sensory information quality at run-time. We show how this enables to tune the circuit's power consumption versus inference accuracy trade-off space with fine granularity, and achieve significant power savings at almost no accuracy loss. In addition, our methodology is able to cope with sensor failure and with a dynamically changing environment by means of an efficient on-line tuning strategy. This dynamic Power-vs-Quality scalability is empirically shown on various Machine Learning benchmarking datasets.
Over the past few years, several self-calibration methodologies have proven their efficiency to calibrate analog and radio-frequency circuits against process variations. Specifically, statistical techniques based on machine-learning have been proposed to recover yield loss and even enhance circuit performances. In addition, these techniques enable to calibrate circuits after a single performance test, i.e. in one-shot. However, towards fully-integrated calibration techniques, the inference part of the machine learning algorithm needs to be performed as energyefficiently as possible to reduce calibration cost to a minimum. Following the path of resource-efficient machine learning, this work explores an alternative to state-of-the-art Neural Network based statistical techniques. Specifically, we investigate the opportunities of using Bayesian Networks for resource-efficient onchip statistical calibration of analog/RF circuits. Results will show that several improvements can be achieved using Bayesian Networks: (a) provide a comprehensive calibration framework with explicit relationships between parameters (b) demonstrate similar prediction accuracies that neural networks (c) optimize across several performance parameters with a single network and in a single query and (d) enable a more energy-efficient hardware implementation. The proposed self-calibration algorithm is applied to a low-noise amplifier fabricated with IBM's 130nm CMOS process, leading to a significant reduction in the number of operations required to obtain the best tuning knob setting.(1) With off-chip post-manufacturing phase (2) With a fully on-chip procedure Off-line training phase On-line (and/or on-chip) phase Optimal Tuning Knobs BUILT-IN TEST
ResumenLas expansiones del lóbulo auricular son cada vez más populares en la población joven, dejando defectos muy notorios que al llegar a la edad adulta requieren reparación. Recientemente se han descrito dos técnicas para solucionar este problema, pero desde nuestro punto de vista, provocan un acortamiento secundario de la oreja. Por esta razón, diseñamos una reparación con dos colgajos, uno medial y otro lateral, donde uno de ellos funciona rellenando el defecto y el otro cubriéndolo; de esta manera evitamos el acortamiento de la oreja y del lóbulo. Además proponemos una nueva clasificación de los defectos del lóbulo auricular. AbstractEarlobe expansions are becoming increasingly popular among young people, leaving very noticeable defects that on reaching adulthood require repair. Recently, two techniques have been described to solve this problem, but in our view, they lead to a shortening of the ear. For this reason we design a repair with two flaps, one medial and one lateral, in which one works by filling the defect and the other covers; so we avoid the shortening of the ear lobe. Furthermore, we propose a new classification of defects in the earlobe. Covarrubias, P.A.Nuevo enfoque para el tratamiento del lóbulo auricular expandido: técnica quirúrgica y clasificación New approachment for the treatment of the expanded ear lobe:surgical technique and classification Trabajo galardonado con el segundo premio en la categoría "Carteles" del 37º Simposio Anual de Residentes y Ex-Residentes del Instituto Jalisciense de Cirugía Reconstructiva "Dr. José Guerrerosantos", año 2010. Guadalajara, México.
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