The Impact of Precision Tuning on Embedded Systems Performance: A Case Study on Field-Oriented Control

Field Oriented Control (FOC) is an industry-standard strategy for controlling induction motors and other kinds of AC-based motors. This control scheme has a very high arithmetic intensity when implemented digitally - in particular it requires the use of trigonometric functions. This requirement contrasts with the necessity of increasing the control step frequency when required, and the minimization of power consumption in applications where conserving battery life is paramount such as drones. However, it also makes FOC well suited for optimization using precision tuning techniques. Therefore, we exploit the state-of-the-art FixM methodology to optimize a miniapp simulating a typical FOC application by applying precision tuning of trigonometric functions. The FixM approach itself was extended in order to implement additional algorithm choices to enable a trade-off between execution time and code size. With the application of FixM on the miniapp, we achieved a speedup up to 278%, at a cost of an error in the output less than 0.1%

Valorizzazione del patrimonio scheletrico umano: una prospettiva su milano

The history and cultural heritage of a city can be evaluated not only through the study of the works of art, artifacts or buildings, but also through the examination of the remains of persons who walked the city in the past millennia. Therefore several thousands of skeletal remains found in Lombardia, especially in Milano, act as cultural assets, though in an the ethical scenario of full respect of human remains. In this way the skeletons tell a history concerning the conditions of health, the richness, culture and even violence, which may confirm, integrate or deny the historical sources when available. Preliminary studies performed on skeletons from different areas of Lombardia have already demonstrated the potential of skeletal material in highlighting for example the evolution of infectious diseases from the Roman age to the Middle Ages, the multiethnicity of Milan at the time of St Ambrose, the heavy labor of children which seems to be present among the Longobards who inhabited the geographic areas of Bergamo as well as Manzoni’s plague affecting the remains found under the Spanish walls. How were they different from us for what concerns life expectancy, diseases, interpersonal violence and lifestyle? In this the skeleton comes through as a true cultural asset.La storia e il patrimonio culturale di una città si misurano non soltanto attraverso lo studio delle opere d’arte, dei manufatti o dell’edilizia, ma anche attraverso l’esame dei resti delle persone che hanno calpestato il suolo nei millenni passati. Ecco quindi che le decine di migliaia di resti scheletrici rinvenuti in Lombardia nelle numerose necropoli portate alla luce fungono anch’esse da “bene culturale”, seppur in una cornice etica e morale del rispetto e del trattamento dignitoso dei resti umani. Gli scheletri in questo modo raccontano una storia sullo stato di salute, la ricchezza, la cultura e persino la violenza, che può confermare, integrare o a volta smentire le fonti storiche quando queste sono disponibili. Studi preliminari effettuati su scheletri di diverse aree della Lombardia, e in particolare Milano, hanno già dimostrato il potenziale del materiale scheletrico nel far intuire ad esempio un’evoluzione delle malattie infettive dall’epoca romana al medioevo, la multietnicità della Milano di Sant’Ambrogio, il pesante lavoro minorile che pare fosse diffuso già tra i longobardi che popolavano la Bergamasca e la peste del Manzoni che affliggeva i resti trovati sotto le mura spagnole di chi già soffriva di malnutrizione. Come si differenziavano da noi e tra di loro nell’aspettativa di vita, nella malattia, nella violenza interpersonale, nello stile di vita? In questo lo scheletro è un vero e proprio bene culturale

The Impact of Profiling Versus Static Analysis in Precision Tuning

Approximate computing techniques, such as precision tuning, are widely recognized as key enablers for the next generation of computing systems, where computation quality metrics play an important role. In precision tuning, a trade-off between the accuracy of computations and latency (and/or energy) is established, but identifying the opportunities for applying this approximate computing technique is often challenging. In this article, we compare two different approaches - worst-case static annotation and profile-guided annotation - and their implications when used in a precision tuning framework. To ensure a fair comparison, we implement the profile-guided approach in an existing tool, TAFFO, and experimentally compare it to the original static approach used by the tool. We validate our considerations using the well-known PolyBench/C benchmark suite, and two real-world application case studies. Our findings demonstrate that the profile-guided approach, fed with reasonable profiling data, in addition to needing less expertise to employ, delivers comparable speedup and better accuracy than the static approach

PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration using Panoptic Attention

A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based methods achieve this goal by using only the geometric information, disregarding the semantics of the scene. In this work, we introduce PADLoC, a LiDAR-based loop closure detection and registration architecture comprising a shared 3D convolutional feature extraction backbone, a global descriptor head for loop closure detection, and a novel transformer-based head for point cloud matching and registration. We present multiple methods for estimating the point-wise matching confidence based on diversity indices. Additionally, to improve forward-backward consistency, we propose the use of two shared matching and registration heads with their source and target inputs swapped by exploiting that the estimated relative transformations must be inverse of each other. Furthermore, we leverage panoptic information during training in the form of a novel loss function that reframes the matching problem as a classification task in the case of the semantic labels and as a graph connectivity assignment for the instance labels. We perform extensive evaluations of PADLoC on multiple real-world datasets demonstrating that it achieves state-of-the-art performance. The code of our work is publicly available at http://padloc.cs.uni-freiburg.de