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Transpiling Python to Rust for Optimized Performance 1

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Lunnikivi, Henri Jylkka, Kai Hamalainen, Timo Tampere Univ Tampere Finland

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

PYTHON has become the de facto programming language in machine learning and scientific computing, but high performance implementations are challenging to create especially for embedded systems with limited resources. We address the challenge of compiling and optimizing PYTHON source code for a low-level target by introducing RUST as an intermediate source code step. We show that pre-existing PYTHON implementations that depend on optimized libraries, such as NumPy, can be transpiled to RUST semi-automatically, with potential for further automation. We use two representative test cases, Black-Scholes for financial options pricing and robot trajectory optimization. The results show up to 12x speedup and 1.5x less memory use on PC, and the same performance but 4x less memory use on an ARM processor on PYNQ SoC FPGA. We also present a comprehensive list of factors for the process, to show the potential for fully automated transpilation. Our findings are generally applicable and can improve the performance of many PYTHON applications while keeping their easy programmability.

关键词： Python Rust embedded computing Transpilation

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Reducing Computation Requirements for Autonomous Mobile Robots on Low Powered embedded systems

Reducing Computation Requirements for Autonomous Mobile Robo...

引用

modeling, simulation & Intelligent Computing (MoSICom), international conference on

作者： Abhinav Pathak Aditya Singhal Adit Tewari Aadish Jain V. Kalaichelvi Department of Computer Science Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani

Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computer vision techniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots.

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Comparison between 2D Classification and 3D Classification based on Deep Learning

Comparison between 2D Classification and 3D Classification b...

引用

embedded systems and Artificial Intelligence (ESAI), international conference on

作者： Zouhir El Khalfi Soulaiman El Hazzat Majid Ben Yakhlef LSI Department of Computer Science Polydisciplinary Faculty of Taza Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

The rise in processing power of computers and the availability of a considerable amount of data have allowed the overlap of several machine learning approaches. This has led to breakthroughs on many deep learning tasks as speech recognition, natural language processing (NLP), object detection and image analysis. Thus, many fields as autonomous driving, industrial quality processes and mobile robotic have released multiples researches in deep learning algorithmic topics. In this paper, we have made a comparison, in terms of accuracy and process timing, between classifications based on 3D points cloud and 2D images for the same input dataset in raw format, and generated basic outlines of both deep learning methods using algorithms based on convolution networks. We discovered that 3D points cloud based classification model was slow on training stage than 2D images model timing due to the 3D complexity data representation. Besides that, classification neural networks based on 3D points cloud data performed better on accuracy than the networks based on 2D images datasets. It is to highlight that 3D based systems are so critical in terms of data processing compared to 2D images due to: Data format as points cloud, meshes and volumes, the hardware resources requirement which is higher due to the third added dimension. Finally, we hypothesize that systems which would combine both of these data geometry and networks architectures would return accuracy better and low processing time wise.

关键词： Point cloud compression Deep learning Solid modeling Three-dimensional displays Accuracy Convolution Natural language processing Data models Timing Classification algorithms

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Tutorial: Large-Scale Spiking Neuromorphic Architecture Exploration using SANA-FE

Tutorial: Large-Scale Spiking Neuromorphic Architecture Expl...

引用

international conference on Hardware/Software Codesign and System Synthesis (CODES)

作者： James A. Boyle Mark Plagge Suma G. Cardwell Frances S. Chanc Andreas Gerstlauer The University of Texas at Austin Texas USA Sandia National Laboratories New Mexico USA

ISBN: (数字)9798350356397

ISBN: (纸本)9798350356403

Neuromorphic computing uses brain-inspired concepts to accelerate and efficiently execute a wide range of applications, such as mimicking biological circuits, solving NPhard optimization problems and accelerating machine learning at the edge. In particular, neuromorphic architectures to efficiently execute Spiking Neural Networks (SNNs) have gained popularity. SNNs extend artificial neural networks (ANNs) by encoding information in time as either rates or delays between spiking events, shared between neurons via their weighted connections. SNN-based platforms are event-driven, resulting in naturally sparse, noise-tolerant and power-efficient computation. In this tutorial, we present the state-of-the-art in scalable digital and analog spiking neuromorphic system architectures, and discuss current research trends within the neuromorphic architecture field at the system level. We further introduce our SANA-FE tool for simulation of Advanced Neuromorphic architectures for Fast Exploration, which has been developed as part of a collaboration between the University of Texas at Austin and Sandia National Laboratories. SANA-FE allows for modeling and performance-power prediction of different spiking hardware architectures executing SNN applications to support rapid, early system-level design-space exploration, hardware-aware application development and system architecture co-design. The tutorial includes a hands-on component in which SANA-FE’s capabilities are demonstrated and used to perform system design and application mapping case studies.

关键词： Neuromorphic engineering Neurons systems architecture computer architecture Tutorials Spiking neural networks Predictive models Market research System analysis and design Optimization

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A Fine-Granularity Image Pyramid Accelerator for embedded Processors 1

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Tsai, Chun-Jen Wang, Chiang-Yi Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

Image-pyramid generator is an important component for many computer vision applications. Due to the complexity of image scaling operations, embedded application processors usually generate three- or four-level image pyramids for vision applications. In this paper, we present an image pyramid accelerator for embedded processors that generates image pyramids of up to 24-levels of down-sampled resolutions for the input image. Since image pyramids are crucial for the coarse-to-fine analysis of many computer vision problems, more resolution levels in the image pyramid can improve the parameter-estimation accuracy. Furthermore, the down-sampling filters used in the proposed design is based on a long-tap Sine-windowed Sinc function filter. Therefore, it preserves more image details than other low-complexity filters such as the bilinear or the bicubic interpolation filter. The proposed circuit is verified on an FPGA development board with a Xilinx Kintex-7 device and will be made open-source. The experimental results show that the design is very promising for real-time computer vision applications.

关键词： Image pyramid Down-sampling filter computer vision Coarse-to-fine processing Hardware-software codesign

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Reconfigurable Planar Electromagnetic Lens Array modeling for Fast Performance Evaluation

Reconfigurable Planar Electromagnetic Lens Array Modeling fo...

引用

Control, Electronics and computer Technology (ICCECT), IEEE international conference on

作者： Daniele Inserra Wanqiang Xu Yuqiong Zhang Zehua Li Essohan-Eyouemou Abalo Fuzhen Xie Guangjun Wen Andrea M. Tonello School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Institute of Networked and Embedded Systems Alpen-Adria-Universität Klagenfurt Klagenfurt Austria

Planar electromagnetic (EM) lenses are thin devices usually comprising sub-wavelength unit cell elements that synthesize a certain phase delay profile to focus the energy at a specific distance from the lens focal length. The design of these planar EM lenses unit cell elements is usually performed by means of full-EM simulation and optimization software. However, the full-EM simulation of the whole planar EM lens for its performance verification has an extremely large computational complexity, as well as it requires a very long simulation time. A mathematical model which describes the behavior of a planar EM lens array based on the characteristics of the EM lens unit cell elements is described in this paper. After that, this model is employed to benchmark the performance of a planar EM lens array based on a reconfigurable transmit array unit cell element, verifying the ability of the lens to reconfigure the focus characteristics to better deal with different incident angles ranges.

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Data Mining in System-Level Design Space Exploration of embedded systems 1

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Richthammer, Valentina Scheinert, Tobias Glass, Michael Ulm Univ Inst Embedded Syst Real Time Syst Ulm Germany

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

With increasingly complex applications and architectures, the task of determining Pareto-optimal implementations at the system level becomes a challenge even for state-of-the-art Design Space Exploration (DSE) methodologies. In this field, nature-inspired techniques such as Evolutionary Algorithms (EAs) are frequently employed, since they are well-suited to the multi-objective and hard-constrained nature of the DSE optimization problem. On the other hand, meta-heuristic approaches are problem-agnostic and are often observed to converge relatively quickly. Furthermore, this type of optimization lacks explainability, i.e. the way in which the optimization algorithm arrives at improved solutions as well as the individual contributions of design decisions to the resulting quality of a solution are not at all clear - and are consequently not utilized during DSE as of yet. To remedy this, we propose the integration of automated data-mining techniques into state-of-the-art DSE flows. Data mining is, thereby, used for (a) the automatic extraction and generation of previously untapped information from the optimization process to be (b) incorporated into the DSE to enhance optimization quality. We present a variety of ways to extract and include relevant knowledge during DSE, as well as (c) several possibilities to gain insight into the interdependence between decision variables and optimization objectives. Experimental results for benchmark systems for large-scale many-cores to networked embedded systems demonstrate the potential of the proposed techniques to improve the quality of the optimized implementations at no DSE-time overhead.

关键词： Design automation Knowledge discovery

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Investigating IoT Application Behaviour in Simulated Fog Environments 1

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10th international conference on Cloud Computing and Services Science (CLOSER)

作者： Markus, Andras Kertesz, Attila Univ Szeged Dept Software Engn Szeged Hungary

ISBN: (数字)9783030723699

ISBN: (纸本)9783030723682;9783030723699

In the past decade novel paradigms appeared in distributed systems, such as Cloud Computing, Fog Computing and the Internet of Things (IoT). Sensors and devices of IoT applications need big data to be stored, processed and analysed, and cloud systems offer suitable and scalable solutions for them. Recently fog nodes are utilized to provide data management functionalities closer to users with enhanced privacy and quality, giving birth to the creation of IoT-Fog-Cloud systems. Such infrastructures are so complex that they need simulators for planning, designing and analysis. Though cloud simulation already has a large number of literature, the simulation of fog systems is still evolving. In this paper we plan to take a step forward in this direction by investigating current fog simulation approaches and compare two of them providing the broadest fog modeling features. We also perform evaluations of executing IoT applications in hybrid, Fog-Cloud architectures to show possible advantages of different setups matching different IoT behaviour.

关键词： Fog computing Internet of Things Cloud computing simulation

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Compiler Optimizations for Safe Insertion of Checkpoints in Intermittently Powered systems 1

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Yarahmadi, Bahram Rohou, Erven Univ Rennes CNRS Inria IRISA Rennes France

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

A large and increasing number of Internet-of-Things devices are not equipped with batteries and harvest energy from their environment. Many of them cannot be physically accessed once they are deployed (embedded in civil engineering structures, sent in the atmosphere or deep in the oceans). When they run out of energy, they stop executing and wait until the energy level reaches a threshold. Programming such devices is challenging in terms of ensuring memory consistency and guaranteeing forward progress. Previous work has proposed to insert checkpoints in the program so that execution can resume from well-defined locations. In this work, we propose to define these checkpoint locations based on statically-computed worst-case energy consumption of code sections. We also apply classical compiler optimizations in order to decrease the required number of checkpoints at runtime. As our method is based upon worst-case energy consumption, we can guarantee memory consistency and forward progress.

关键词： Worst-case energy consumption Static analysis Energy harvesting Checkpoint

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Comparing the Consistency of User Studies Conducted in simulations and Laboratory Settings

Comparing the Consistency of User Studies Conducted in Simul...

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IEEE international conference on Robotic Computing (IRC)

作者： Jonathan Hümmer Dominik Riedelbauch Dominik Henrich Chair for Applied Computer Science III (Robotics and Embedded Systems) University of Bayreuth Bayreuth Germany

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

Human-robot collaboration enables highly adaptive co-working. The variety of resulting workflows makes it difficult to measure metrics as, e.g. makespans or idle times for multiple systems and tasks in a comparable manner. This issue can be addressed with virtual commissioning, where arbitrary numbers of non-deterministic human-robot workflows in assembly tasks can be simulated. To this end, data-driven models of human decisions are needed. Gathering the required large corpus of data with on-site user studies is quite time-consuming. In comparison, simulation-based studies (e.g., by crowdsourcing) would allow us to access a large pool of study participants with less effort. To rely on respective study results, human action sequences observed in a browser-based simulation environment must be shown to match those gathered in a laboratory setting. To this end, this work aims to understand to what extent cooperative assembly work in a simulated environment differs from that in an on-site laboratory setting. We show how a simulation environment can be aligned with a laboratory setting in which a robot and a human perform pick-and-place tasks together. A user study (N=29) indicates that participants' assembly decisions and perception of the situation are consistent across these different environments.

关键词： Crowdsourcing Cognitive processes Atmospheric measurements Computational modeling Laboratories Human-robot interaction Particle measurements Safety Assembly Robots

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