Methoden zur applikationsspezifischen Verlustleitungsoptimierung für eingebettete Prozessoren

Dieser Beitrag beschreibt eine Methodik zur Verlustleistungsmodellierung von eingebetteten Prozessoren im Entwurfsstadium auf Basis der Hardwarebeschreibung. Die Methodik wurde exemplarisch auf einen typischen RISC-Prozessor angewendet. Die gewonnenen Verlustleistungsmodelle zeigen eine geringe Abweichung hinsichtlich der mittleren Verlustleistungsaufnahme von unter 5% und eine hohe Güte bezüglich des zeitlichen Verlaufes der Verlustleistungsaufnahme im Vergleich zur sehr zeitaufwendigen Simulation der Gatter-Netzliste. Zudem lassen sich die Modelle zusammen mit der funktionalen Emulation des Prozessors auf einem FPGA abbilden. Die hohe Ausführungsgeschwindigkeit der Emulation erlaubt sowohl eine umfassende, verlustleistungsorientierte Optimierung der Anwendungen durch den Applikationsentwickler als auch eine anwendungsorientierte Optimierung der Prozessorarchitektur durch den Hardwareentwickler

Modeling and Error Analysis in Camera-Based Jump Height Measurement

Introduction: In this work, we use simulated data to quantify the different failure mechanisms of a previously presented low-cost jump height measurement system, based on widely available consumer smartphone technology. Methods: In order to assess the importance of the different preconditions of the jump height measurement algorithm, we generate a synthetic dataset of 2000 random jump parabolas for 2000 randomly generated persons without real-world artifacts. We then selectively add different perturbations to the parabolas and reconstruct the jump height using the evaluated algorithm. The degree to which the manipulations influence the reconstructed jump height gives us insights into how critical each precondition is for the method’s accuracy. Results: For a subject-to-camera distance of 2.5 meters, we found the most important influences to be tracking inaccuracies and distance changes (non-vertical jumps). These are also the most difficult factors to control. Camera angle and lens distortion are easier to handle in practice and have a very low impact on the reconstructed jump height. The intraclass correlation value ICC(3,1) between true jump height and the reconstruction from distorted data ranges between 0.999 for mild and 0.988 for more severe distortions. Conclusion: Our results support the design of future studies and tools for accurate and affordable jump height measurement, which can be used in individual fitness, sports medicine, and rehabilitation applications

Markerless camera-based vertical jump height measurement using OpenPose

Vertical jump height is an important tool to measure athletes’ lower body power in sports science and medicine. This work improves upon a previously published self-calibrating algorithm, which determines jump height using a single smartphone camera. The algorithm uses the parabolic fall trajectory obtained by tracking a single feature in a high-speed video. Instead of tracking an ArUco marker, which must be attached to the jumping subject, this work uses the OpenPose neural network for human pose estimation in order to calculate an approximation of the body center of mass. Jump heights obtained this way are compared to the reference heights from a motion capture system and to the results of the original work. The result is a trade-off between increased ease-of-use and slightly diminished accuracy of the jump height measurement

Wahrscheinlichkeitsbasierte Methoden zur autonomen Führung von Fahrzeugen in unsicherer Umgebung

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A Survey on Application Specific Processor Architectures for Digital Hearing Aids

On the one hand, processors for hearing aids are highly specialized for audio processing, on the other hand they have to meet challenging hardware restrictions. This paper aims to provide an overview of the requirements, architectures, and implementations of these processors. Special attention is given to the increasingly common application-specific instruction-set processors (ASIPs). The main focus of this paper lies on hardware-related aspects such as the processor architecture, the interfaces, the application specific integrated circuit (ASIC) technology, and the operating conditions. The different hearing aid implementations are compared in terms of power consumption, silicon area, and computing performance for the algorithms used. Challenges for the design of future hearing aid processors are discussed based on current trends and developments

Fixed Point Analysis Workflow for efficient Design of Convolutional Neural Networks in Hearing Aids

Neural networks (NN) are a powerful tool to tackle complex problems in hearing aid research, but their use on hearing aid hardware is currently limited by memory and processing power. To enable the training with these constrains, a fixed point analysis and a memory friendly power of two quantization (replacing multiplications with shift operations) scheme has been implemented extending TensorFlow, a standard framework for training neural networks, and the Qkeras package [1, 2]. The implemented fixed point analysis detects quantization issues like overflows, underflows, precision problems and zero gradients. The analysis is done for each layer in every epoch for weights, biases and activations respectively. With this information the quantization can be optimized, e.g. by modifying the bit width, number of integer bits or the quantization scheme to a power of two quantization. To demonstrate the applicability of this method a case study has been conducted. Therefore a CNN has been trained to predict the Ideal Ratio Mask (IRM) for noise reduction in audio signals. The dataset consists of speech samples from the TIMIT dataset mixed with noise from the Urban Sound 8kand VAD-dataset at 0 dB SNR. The CNN was trained in floating point, fixed point and a power of two quantization. The CNN architecture consists of six convolutional layers followed by three dense layers. From initially 1.9 MB memory footprint for 468k float32 weights, the power of two quantized network is reduced to 236 kB, while the Short Term Objective Intelligibility (STOI) Improvement drops only from 0.074 to 0.067. Despite the quantization only a minimal drop in performance was observed, while saving up to 87.5 % of memory, thus being suited for employment in a hearing ai

Эффективность замены парового турбопривода механизмов собственных нужд энергоблоков ТЭС газотурбинным приводом

Получено уравнение, позволяющее на основе срока окупаемости определить экономическую целесообразность замены парового турбопривода механизма собственных нужд энергоблоков ТЭС газотурбинным двигателем в зависимости от стоимости электроэнергии и топлива и стоимостных и режимных показателей газотурбинных двигателей

Optimized Minimum-Search for SAR Backprojection Autofocus on GPUs Using CUDA

Autofocus techniques for synthetic aperture radar (SAR) can improve the image quality substantially. Their high computational complexity imposes a challenge when employing them in runtime-critical implementations. This paper presents an autofocus implementation for stripmap SAR specially optimized for parallel architectures like GPUs. Thorough evaluation using real SAR data shows that the tunable parameters of the algorithm allow to counterbalance runtime and achieved image quality.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Measuring vertical jump height using a smartphone camera with simultaneous gravity-based calibration

Vertical jump height is an important tool to measure athletes' lower body power in sports science and medicine. Several different methods exist to measure jump height, but each has its own limitations. This work proposes a novel way to measure jump height directly, using optical tracking with a single smartphone camera. A parabolic fall trajectory is obtained from this video by tracking a single feature. The parabolic trajectory is then used to partially calibrate the camera and convert pixel measurements into real-world units, allowing the calculation of the achieved height. Comparison to an optical motion capture system yields promising results.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works