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Lynx: A SmartNIC-driven Accelerator-centric Architecture for network Servers 20

Lynx: A SmartNIC-driven Accelerator-centric Architecture for...

引用

25th International conference on Architectural Support for programming Languages and Operating Systems (ASPLOS)

作者： Tork, Maroun Maudlej, Lina Silberstein, Mark Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781450371025

This paper explores new opportunities afforded by the growing deployment of compute and I/O accelerators to improve the performance and efficiency of hardware-accelerated computing services in data centers. We propose Lynx, an accelerator-centric network server architecture that offloads the server data and control planes to the SmartNIC, and enables direct networking from accelerators via a lightweight hardware-friendly I/O mechanism. Lynx enables the design of hardware-accelerated network servers that run without CPU involvement, freeing CPU cores and improving performance isolation for accelerated services. It is portable across accelerator architectures and allows the management of both local and remote accelerators, seamlessly scaling beyond a single physical machine. We implement and evaluate Lynx on GPUs and the Intel Visual Compute Accelerator, as well as two SmartNIC architectures - one with an FPGA, and another with an 8-core ARM processor. Compared to a traditional host-centric approach, Lynx achieves over 4x higher throughput for a GPU-centric face verification server, where it is used for GPU communications with an external database, and 25% higher throughput for a GPU-accelerated neural network inference service. For this workload, we show that a single SmartNIC may drive 4 local and 8 remote GPUs while achieving linear performance scaling without using the host CPU.

关键词： smartnics i/o services for accelerators hardware accelerators operating systems server architecture

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Scalable Autonomous Vehicle Safety Validation through Dynamic programming and Scene Decomposition 23

Scalable Autonomous Vehicle Safety Validation through Dynami...

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23rd ieee International conference on Intelligent Transportation Systems (ITSC)

作者： Corso, Anthony Lee, Ritchie Kochenderfer, Mykel J. Stanford Univ Aeronaut & Astronaut Dept Stanford CA 94305 USA NASA Ames Res Ctr Robust Software Engn Grp Moffett Field CA 94035 USA

ISBN: (纸本)9781728141497

An open question in autonomous driving is how best to use simulation to validate the safety of autonomous vehicles. Existing techniques rely on simulated rollouts, which can be inefficient for finding rare failure events, while other techniques are designed to only discover a single failure. In this work, we present a new safety validation approach that attempts to estimate the distribution over failures of an autonomous policy using approximate dynamic programming. Knowledge of this distribution allows for the efficient discovery of many failure examples. To address the problem of scalability, we decompose complex driving scenarios into subproblems consisting of only the ego vehicle and one other vehicle. These subproblems can be solved with approximate dynamic programming and their solutions are recombined to approximate the solution to the full scenario. We apply our approach to a simple two-vehicle scenario to demonstrate the technique as well as a more complex five-vehicle scenario to demonstrate scalability. In both experiments, we observed an increase in the number of failures discovered compared to baseline approaches.

关键词： Autonomous vehicles

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Research on the Impact of open Residential Roads on Traffic Capability 5

Research on the Impact of Open Residential Roads on Traffic ...

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ieee 5th International conference on Computer and Communication Systems (ICCCS)

作者： Qian, Qi-pei Bao, Kai-xuan Zhao, Kai Lu, Yin Nanjing Univ Posts & Telecommun Bell Honors Sch Nanjing Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing Peoples R China

ISBN: (纸本)9781728161365

Predicting the impact of open residential roads will have significance not only for transportation planning and economic development, but also for the enhancement of human society. Aiming at the problem of different road and community structure, this paper considers the road length, road speed limit, traffic volume, traffic lights, and other factors to improve the traffic capability. Then uses nonlinear programming to obtain the optimal solution based on BPR road impedance model. Finally, based on different residential road structures and traffic pressure, the enhancement of open residential roads is compared to find out which road structure is optimal for opening.

关键词： component open residential roads BPR road impedance model nonlinear programming

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Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge

Now that I can see, I can improve: Enabling data-driven fine...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Rajagopal, Aditya Bouganis, Christos-Savvas Imperial Coll London London England

ISBN: (纸本)9781728193601

In today's world, a vast amount of data is being generated by edge devices that can be used as valuable training data to improve the performance of machine learning algorithms in terms of the achieved accuracy or to reduce the compute requirements of the model. However, due to user data privacy concerns as well as storage and communication bandwidth limitations, this data cannot be moved from the device to the data centre for further improvement of the model and subsequent deployment. As such there is a need for increased edge intelligence, where the deployed models can be fine-tuned on the edge, leading to improved accuracy and/or reducing the model's workload as well as its memory and power footprint. In the case of Convolutional Neural networks (CNNs), both the weights of the network as well as its topology can be tuned to adapt to the data that it processes. This paper provides a first step towards enabling CNN finetuning on an edge device based on structured pruning. It explores the performance gains and costs of doing so and presents an extensible open-source framework that allows the deployment of such approaches on a wide range of network architectures and devices. The results show that on average, data-aware pruning with retraining can provide 10.2pp increased accuracy over a wide range of subsets, networks and pruning levels with a maximum improvement of 42.0pp over pruning and retraining in a manner agnostic to the data being processed by the network.

关键词： network architecture

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UV-Induced Degradation of High-Efficiency Solar Cells with Different architectures 47

UV-Induced Degradation of High-Efficiency Solar Cells with D...

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47th ieee Photovoltaic Specialists conference (PVSC)

作者： Sinha, Archana Qian, Jiadong Hurst, Katherine Moffitt, Stephanie L. Schelhas, Laura T. Miller, David C. Hacke, Peter SLAC Natl Accelerator Lab Menlo Pk CA 94025 USA Natl Renewable Energy Lab Golden CO USA

ISBN: (纸本)9781728161150

The ultraviolet-induced degradation (UVID) of solar panels is associated with the deterioration of cell performance and reduced reliability of packaging materials. Here we examine the UV stability of different architectures of high-efficiency solar cells without any encapsulation. Identical UV exposure tests were performed at two different labs using UVA-340 fluorescent lamps under different electrical bias configurations (open- or short-circuit) and irradiated cell surfaces (for bifacial technologies). Cell technologies, including heterojunction (HJ), interdigitated back-contact cells (IBC), passivated emitter rear contact (PERC), passivated emitter rear totally-diffused (PERT), are found to be more susceptible to UVID, leading to significant I-sc loss (up to 4%) and P-max loss (up to 15%) as compared to the conventional back surface field (Al-BSF) cells after exposure to UV irradiation of 8.92 MJ m(-2).nm(-1) at 340 nm. Additionally, the bifacial cells when irradiated from the backside exhibited greater photocurrent loss compared to the front side exposure, indicating potential sensitivity of rear surface passivation to UV radiation.

关键词： crystalline silicon UV induced degradation performance loss surface passivation UV exposure

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Application of Fuzzy Answer set programming in Hybrid Deep Neural-Symbolic Architecture 1

Application of Fuzzy Answer set Programming in Hybrid Deep N...

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1st ieee International conference for Convergence in Engineering, ICCE 2020

作者： Paul, Sandip Ray, Kumar Sankar Saha, Diganta Indian Statistical Institute ECSU Kolkata India Jadavpur University CSE Department Kolkata India

ISBN: (纸本)9781728173405

Hybrid deep neural-symbolic architecture for event-detection employs a deep neural network at the back-end to perform low-level reasoning and a symbolic logical module to perform high-level cognitive reasoning. The currently known hybrid architectures use classical Answer Set programming(ASP), which is unable to perform fuzzy reasoning with uncertainty. Moreover these systems don't extract new rules from the available data. On the other hand, there are neuro-fuzzy systems that can extract fuzzy rules from data by means of Gaussian Restricted Boltzman Machines (GRBM). Both the aspects should be merged together to achieve human-like intelligent reasoning and learning from environment. But the success of such an integration depends upon the chosen logical system, that can support fuzzy reasoning with uncertainty, as well as, can support the extracted knowledge from GRBM. This work investigates the feasibility of using interval-valued fuzzy logic programming for this purpose. This work focuses on the theoretical aspects from logic programming perspective. © 2020 ieee.

关键词： Fuzzy inference

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Recognition of DNA Secondary Structures as Nucleosome Barriers with Deep Learning Methods

Recognition of DNA Secondary Structures as Nucleosome Barrie...

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ieee International conference on Bioinformatics and Biomedicine (ieee BIBM)

作者： Pavlov, Fedor Poptsova, Maria Natl Res Univ Higher Sch Econ Moscow Russia

ISBN: (纸本)9781728162157

Over the past few years, genome research using machine and in-depth learning techniques has become increasingly popular, and researchers are being provided with sophisticated data analysis tools. Recognition of patterns of DNA secondary structures and genomic functional elements are still poorly investigated, despite the fact that research in this area has the potential to contribute greatly to the development of medicine and pharmacology. This study aims to explore machine and deep learning methods that have proven to be successful in natural language processing with respect to the task of DNA sequence recognition. Two deep learning models based on CNN and LSTM architectures were developed. Each model was tested on multiple classification tasks for recognition of DNA sequences containing quadruplexes with potential function of nucleosome barriers. Additionally, model interpretation analysis was performed in the form of extraction of CNN significant filters and their transformation into DNA-motifs.

关键词： deep learning deep neural network interpretation G-quadruplex nucleosome barriers non-B DNA structures

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open-Source Power Outlet System for IoT-based Smart Homes 3

Open-Source Power Outlet System for IoT-based Smart Homes

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3rd International conference on Smart Energy Systems and Technologies (SEST)

作者： Dias, Tiago Sampaio, Pedro Cardoso, Diogo Prates, Claudio Jesus, Ricardo Costa, Tiago Inst Politecn Lisboa IPL Inst Super Engn Lisboa ISEL Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781728147017

This paper presents an open-source wireless Smart Power Outlet (SPO) system to improve the control and monitoring of household appliances in IoT-based smart homes. The system supports not only the most typical SPO functionalities, such as remote on/off control or real-time monitoring of the current consumption, but also other important functionalities, like programming of the power supply time schedule, automatic interruption of vampire currents, or prevention of electrocutions. Moreover, the devised system architecture allows to connect multiple SPOs, even with different capabilities, in an ieee 802.15.4 Sub-1 GHz Personal Area network (PAN). This network is managed by a Controller that also acts as a border router, providing remote access to the SPOs. A prof-of-concept prototype of the system has been implemented and tested. The considered SPO and Controller were based on SimpleLink Sub-1 GHz CC1310 wireless microcontrollers running the Contiki OS. The obtained results demonstrate the basic functionalities offered by the proposed open-source SPO and suggest that it can be used to make the next generation of homes smarter at lower costs.

关键词： IoT Smart home Power outlet system CoAP ieee 802.15.4 Contiki OS

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Non-architectural improvements for ECG classification using deep neural network

Non-architectural improvements for ECG classification using ...

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Ivannikov Ispras open conference (ISPRAS)

作者： Andreev, Pavel Ananev, Vladislav Avetisyan, Aram Makarov, Vladimir Gliner, Vadim Schuster, Assaf Karpulevich, Evgeny Russian Acad Sci Ivannikov Inst Syst Programming Moscow Russia Novgorod State Univ Veliky Novgorod Russia Kurchatov Inst Natl Res Ctr Moscow Russia Moscow Inst Phys & Technol Genome Technol & Bioinfonnat Res Ctr Dolgoprudnyi Russia Technion IIT Comp Sci Dept Haifa Israel Skolkovo Inst Sci & Technol Moscow Russia Natl Res Univ Higher Sch Econ Moscow Russia Lomonosov Moscow State Univ Moscow Russia

ISBN: (纸本)9781665412919

Due to the latest advances in machine learning algorithms new deep learning-based approaches to the interpretation of 12-lead electrocardiograms have been developed, demonstrating the quality of diagnostics comparable to the expert one. In this paper, we propose several techniques increasing the quality of ECG classification by a deep neural network. The techniques include patient metadata incorporation, signal denoising and self-adaptive model training. The experimental validation of the approaches was carried out on a novel dataset containing 64198 standard ECG recordings obtained during routine medical practice. The conducted experiments demonstrated statistically significant quality growth compared to the baseline, supporting the further application of our findings.

关键词： ECG Classification Convolutional Neural network Deep Learning Denoising Self-Adaptive Learning

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Balancing Data Freshness and Distortion in Real-time Status Updating with Lossy Compression

Balancing Data Freshness and Distortion in Real-time Status ...

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39th ieee International conference on Computer Communications (ieee INFOCOM)

作者： Hu, Shaoling Chen, Wei Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781728186955

Real-time status updating plays a pivotal role in automated control, situational awareness, and networked monitoring. However, how to balance data freshness and its distortion due to lossy compression remains open. In this paper, we are interested in the optimal cross-layer policy that minimizes the Age-of-Information (AoI) and distortion simultaneously over an ON/OFF channel. Specifically, we formulate a hierarchical framework to jointly schedule the lossy compression in the application layer and the packet scheduling in the physical layer. In the application layer, we characterize the compression loss using age or forgetting factor weighted distortion. The optimal tradeoff between the age reduction and compression loss is then revealed via convex optimizations. It allows us to further determine how many packets to send in the physical layer based on a probabilistic scheduling policy. To this end, a Constrained Markov Decision Process (CMDP) problem is formulated and solved by Linear programming (LP), which gives the optimal tradeoff between the data freshness and distortion, as well as, the optimal cross-layer strategy to strike the AoI-distortion tradeoff.

关键词： Linear programming

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