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ActIPP: Active Intellectual Property Protection of Edge-Level Graph Learning for distributed Vehicular Networks 22

ActIPP: Active Intellectual Property Protection of Edge-Leve...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Yang, Xiao Li, Gaolei Dong, Mianxiong Ota, Kaoru Zhang, Chaofeng Peng, Xiting Li, Jianhua Shanghai Jiao Tong University China Muroran Institute of Technology Japan Advanced Institute of Industrial Technology China Shenyang University of Technology China

ISBN: (纸本)9798331509712

Edge-Level Graph Learning System (EGLS) exhibits diverse applicability in management of distributed vehicular networks, e.g., flow prediction, route planning, and accident forecasting. For the EGLS training, expensive hardware resource consumption, traffic data collection, and dedicated training procedures make the learning algorithms become valuable intellectual property (IP) for the EGLS owner (e.g., Uber and Lyft), and they cannot tolerate the infringement act of their models' intellectual property. To enhance its IP protection, we present ActIPP, the first active IP protection methodology for EGLS, which incorporates a built-in access control function in the model to safeguard against unauthorized queries. Specifically, it is achieved via a creative edge backdoor mechanism, wherein the edge training samples are poisoned via user-specific access tokens to induce legal outputs from a well-trained EGLS model for authorized users. Moreover, related token regulating strategies were proposed to dynamically realize the addition and revocation of user tokens by model retraining to guarantee access control in EGLS. Additionally, a Graph Mutual Information-based adaptive token generation method is presented to augment the access control embedding. Based on experiments with various real-world datasets, ActIPP demonstrates high success rates of IP protection (accuracy drop © 2024 IEEE.

关键词： Access control models

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FlexScience'22: 12th Workshop on AI and Scientific computing at Scale using Flexible computing Infrastructures 31

FlexScience'22: 12th Workshop on AI and Scientific Computing...

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31st international symposium on High-Performance parallel and distributed computing, HPDC 2022

作者： Ferrucci, Luca Coppola, Massimo Kavalionak, Hanna Kontopoulos, Ioannis Consiglio Nazionale Delle Ricerche Pisa Italy Harokopio University of Athens Athens Greece

ISBN: (纸本)9781450391993

The 2nd international Workshop on Flexible Resource and Application Management on the Edge (FRAME 2022) is dedicated to the so-called Cloud/Edge Continuum, where Cloud and Edge infrastructures can work together to fulfill requirements from a variety of NextGen applications. Clouds provide appropriate levels of performance to large groups of different users, whereas Edge resources act as a first layer of computing capacity that is closer to the user, to reduce the service latency. With respect to Clouds, Edge infrastructures typically are composed of heterogeneous and constrained resources and introduce new challenges from the viewpoint of security, orchestration and resource management. Tackling these new issues calls for innovative combinations of tools and abstractions, where AI and machine learning techniques complement algorithmic orchestration and optimization, bringing about new levels of distributed adaptivity and self-management. As real-time data-driven decisions can be promptly taken on the spot, without the need to wait for data to travel to the Cloud and back, also interactive and time-sensitive services like the immersive data processing of Extended Reality (XR) applications can be partially extended toward the edge, thus exploiting a better computation to communication tradeoff and smoother connections to improve their QoE and remote collaboration. The FRAME'22 workshop proceedings are available at: https://***/***?id=3526059. © 2022 Owner/Author.

关键词： Edge computing

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FrameFeedback: A Closed-Loop Control System for Dynamic Offloading Real-Time Edge Inference

FrameFeedback: A Closed-Loop Control System for Dynamic Offl...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Jackson, Matthew It, Bo Nikolopoulos, Dimitrios S. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)9798350364613;9798350364606

Despite the demand for real-time deep learning applications such as video analytics at the edge, resource-constrained edge devices can largely not process video streams at their source frame rate. However, deep learning execution can be accelerated by offloading tasks to a nearby edge server equipped with a GPU. For a realistic edge system with variable network conditions and server load, we consider optimally partitioning the frames from a video stream between local processing and offloading to maximize the throughput of an edge device under a real-time deadline. To do this, we show that we can simplify the influences on processing latency into a single relevant metric and dynamically determine an appropriate offloading rate using a latency-based feedback control mechanism. Our controller settles on the optimal offloading rate without knowing network conditions, resource availability, or application computation cost. Our measurements show that our feedback controller balances sensitivity and overcorrection given a variety of network and load conditions set. We also show that our controller's Quality of Service outperforms state-of-the-art baselines and approaches.

关键词： edge computing adaptive offloading real-time deep learning feedback control

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TaPaSCo-AIE: An Open-Source Framework for Streaming-based Heterogeneous Acceleration using AMD AI Engines

TaPaSCo-AIE: An Open-Source Framework for Streaming-based He...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Heinz, Carsten Kalkhof, Torben Lavan, Yannick Koch, Andreas Tech Univ Darmstadt Embedded Syst & Applicat Grp Darmstadt Germany

ISBN: (纸本)9798350364613;9798350364606

AMD AI Engines (AIEs) extend the design space and open up new options for coarse -grained processing in re-configurable accelerators. Pure FPGA designs for machine learning often struggle to compete with the high clock frequencies of GPUs for data -intensive workloads with only limited control flow. Having AIEs available on-chip with an FPGA fabric allows for low -latency co -processing and permits parts of an application to be placed on the most suitable kind of processing unit. Many data -heavy workloads, particularly in the AI domain, benefit from data streaming. With TaPaSCo-AIE, we present a framework for heterogeneous systems centered around data streams. Our framework focuses on AMD Versal devices and incorporates AI Engines and 100G network. We demonstrate the efficient use of TaPaSCo-AIE in a real -world evaluation based on a neural network, and achieve significant performance improvements over CPUs, and even exceed the performance of an AIM GPU.

关键词： FPGA Versal AI Engine Heterogeneous computing Network

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HiPS2022: The 2nd Workshop on High Performance Serverless computing 31

HiPS2022: The 2nd Workshop on High Performance Serverless Co...

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31st international symposium on High-Performance parallel and distributed computing, HPDC 2022

作者： Narasimhamurthy, Sai Lockwood, Glenn Seagate Systems Marlow United Kingdom Lawrence Berkeley National Laboratory BerkeleyCA United States

ISBN: (纸本)9781450391993

Exciting changes are coming to the fore in the world of Storage and I/O for High Performance and Data Intensive computing. Existing data access and data retrieval methods that suitably support the new generation of data intensive applications in the realm of HPC and AI are being re-assessed. These new applications need to support both evolutionary as well as revolutionary approaches to data access and storage. Open source is enabling the adoption of these new techniques. This workshop looks at cutting edge trends in storage systems and solutions for data intensive HPC and AI applications with specific focus primarily on community driven initiatives and commercial products that may inspire the community. © 2022 Owner/Author.

关键词： Digital storage

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A Workflow Scheduling Algorithm Based on Linear Evaluation for Dynamic distributed computing Environments

A Workflow Scheduling Algorithm Based on Linear Evaluation f...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xin Li Zhan Zeng Yuxin Chen Jinglong Deng Yue Hong Wei Zheng Department of Computer Science Xiamen University Xiamen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The development of computer architecture towards multi-core processors brings new opportunities and challenges for efficient task scheduling in scientific workflows. This paper investigates the key challenges of scheduling scientific workflows, typically represented as Directed Acyclic Graphs (DAGs), in multi-core and dynamic distributed computing environments. Traditional static and dynamic scheduling algorithms face limitations in handling complex DAG structures and complex computational environments. To address these issues, we propose an improved LinearPB algorithm that simplifies the node priority calculation through linear evaluation, thereby reducing computational overhead and enhancing scheduling performance. Our extensive experiments demonstrate that the LinearPB algorithm significantly reduces execution time, outperforms existing algorithms in most cases.

关键词： Directed acyclic graph Runtime Scheduling algorithms Multicore processing Heuristic algorithms Computer architecture Dynamic scheduling distributed computing Faces Edge computing

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An Efficient Data Structure for Sparse Bit-Vectors with Applications in Neuromorphic computing 28

An Efficient Data Structure for Sparse Bit-Vectors with Appl...

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28th IEEE international symposium on Asynchronous Circuits and Systems (ASYNC)

作者： Purohit, Prafull Leugering, Johannes Manohar, Rajit Yale Univ Elect Engn New Haven CT 06520 USA Fraunhofer Inst Integrated Circuits IIS Broadband & Broadcast Dept Erlangen Germany

ISBN: (纸本)9798350305760

Iterating over all the elements of a set is a very common problem in highly parallel systems. In hardware, this is typically realized by either storing the set-membership of each element in a fixed-length bit-vector or by storing just the indices of the members in a dynamically sized queue. However, the former solution is only efficient in terms of memory and runtime if each element is a member of the set with approximately 50% probability, whereas the latter is only efficient if the set is extremely sparse. We propose an alternative asynchronous, concurrent, distributed data structure based on a binary tree topology that is more efficient for sets with sparsity in between these two extremes. The proposed structure allows us to construct a set by adding individual elements one at a time in arbitrary order, and to iterate over all these elements exactly once, clearing the set in the process. We analyzed this data structure, simulated its behavior in CHP, synthesized it into an asynchronous digital circuit, optimized the circuits, and performed SPICE simulation to evaluate our design. The results confirm that our proposed structure offers a low-latency, low-power solution for moderately sparse data, and may thus prove useful for asynchronous and neuromorphic systems.

关键词： data structure neuromorphic systems asynchronous circuits

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A Dynamic distributed Scheduler for computing on the Edge

A Dynamic Distributed Scheduler for Computing on the Edge

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Fei Hu Kunal Mehta Shivakant Mishra Mohammad AlMutawa Dept. of Computer Science University of Colorado Dept. of Computer Science Kuwait University

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Edge computing is crucial for IoT applications, especially those needing quick, private data handling. However, these applications are resource-intensive, and edge computing resources are limited compared to cloud capabilities. Efficiently using these limited resources is essential to meet all application demands, including latency, privacy, and cost. Due to the dynamic and hybrid nature of IoT environments, static scheduling systems often falls short in meeting these diverse constraints. This paper describes the design, implementation, and evaluation of a dynamic distributed scheduler for the edge. This scheduler schedules the execution of various computing tasks across all computing resources available at the edge in order to satisfy the diverse constraints of an IoT application. The key characteristic of this scheduler is that it constantly monitors the current state of the IoT infrastructure and dynamically adjusts the scheduling of various computing tasks based on the current environmental context as well as the current computing and network conditions. The scheduler utilizes a predictive profiling method to manage the hybrid and variable nature of edge computing resources and tasks. A prototype of this scheduler has been implemented and the paper demonstrates its practicality via an augmented reality application.

关键词： Schedules Privacy Processor scheduling Prototypes parallel processing Dynamic scheduling Real-time systems Internet of Things Resource management Edge computing

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parallel Derandomization for Coloring*

Parallel Derandomization for Coloring*

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international symposium on parallel and distributed Processing (IPDPS)

作者： Sam Coy Artur Czumaj Peter Davies-Peck Gopinath Mishra Department of Computer Science University of Warwick Coventry United Kingdom Department of Computer Science Durham University Durham United Kingdom Department of Computer Science National University of Singapore Singapore

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Graph coloring problems are among the most fundamental problems in parallel and distributed computing, and have been studied extensively in both settings. In this context, designing efficient deterministic algorithms for these problems has been found particularly *** this work we consider this challenge, and design a novel framework for derandomizing algorithms for coloring-type problems in the Massively parallel Computation (MPC) model with sublinear space. We give an application of this framework by showing that a recent (degree + 1) -list coloring algorithm by Halldorsson et al. (STOC’22) in the LOCAL model of distributed computation can be translated to the MPC model and efficiently derandomized. Our algorithm runs in O (log log log n) rounds, which matches the complexity of the state of the art algorithm for the (Δ + 1)-coloring problem.

关键词： distributed processing Computational modeling Computational efficiency Complexity theory distributed computing Context modeling

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parallel Swarm Propagation for Neural Networks

Parallel Swarm Propagation for Neural Networks

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Arghya Bhattacharya V Premanand School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Deep learning has revolutionized various fields but faces challenges such as limitations owing to vanishing gradients and the inability to handle non-differentiable loss functions and layers. Our study proposes parallel Swarm Propagation for Neural Networks, a novel approach that leverages parallel particle swarm optimization (PSO) for optimizing neural networks. PSO does not require gradient calculations and effectively circumvents the vanishing gradient problem. This enables the exploration of non-differentiable loss functions and layers, potentially leading to more robust and flexible deep-learning models. Additionally, the inherent parallelism of PSO allows for significant performance gains by utilizing the ever-increasing processing power of parallel-computing architectures. Our study investigates the efficacy of parallel PSO in optimizing neural networks, analyzing its performance compared to traditional gradient-based methods, and exploring its potential for unlocking new possibilities in deep learning.

关键词： Backpropagation Training Deep learning Scalability Neural networks Graphics processing units parallel processing Network architecture Hardware Particle swarm optimization

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