The feasibility of efficient scheduling of real-timeapplications with different degrees of criticality in safety-involved systems is considered. The object of the study is the scheduling of tasks the solution of whic...
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Autonomous quadcopter control based on computer vision has become one of the popular research methods. However, challenges persist due to constraints in processing speed and memory associated with embedded edge comput...
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Achieving efficient and consistent localization with a prior map remains challenging in robotics. Conventional keyframe-based approaches often suffer from sub-optimal viewpoints due to limited field of view (FOV) and/...
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ISBN:
(纸本)9798350384581;9798350384574
Achieving efficient and consistent localization with a prior map remains challenging in robotics. Conventional keyframe-based approaches often suffer from sub-optimal viewpoints due to limited field of view (FOV) and/or constrained motion, thus degrading the localization performance. To address this issue, we design a real-time tightly-coupled Neural Radiance Fields (NeRF)-aided visual-inertial navigation system (VINS). In particular, by effectively leveraging the NeRF's potential to synthesize novel views, the proposed NeRF-VINS overcomes the limitations of traditional keyframe-based maps (with limited views) and optimally fuses IMU, monocular images, and synthetically rendered images within an efficient filter-based framework. This tightly-coupled fusion enables efficient 3D motion tracking with bounded errors. We extensively validate the proposed NeRF-VINS against the state-of-the-art methods that use prior map information, and demonstrate its ability to perform real-time localization, at 15 Hz, on a resource-constrained Jetson AGX Orin embedded platform.
Now a days in the automotive industry there are lot of issues in production sectors. The topics of cables and its testing are covered in it. During production time there are more issues such as the need of man power t...
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Recent impressive growth of AI applications in the most diversified heterogeneous domains is largely motivated by the availability of hardware accelerators used from the backstage of data centers (such as TPU, Tensor ...
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ISBN:
(纸本)9781538674628
Recent impressive growth of AI applications in the most diversified heterogeneous domains is largely motivated by the availability of hardware accelerators used from the backstage of data centers (such as TPU, Tensor Processing Units, or VPUs, Visual Processing Units) to the far edge of embedded devices equipped with DPUs and Deep Learning Processing Units. High level toolchains for a more friendly usability of these platform had similar relevance in the process. In this paper we considered edge devices that provide an essential contribution for the deployment of "distributed intelligence" and are used typically at the gateway, CPE or Edge computing level. One of the typical assumptions is that Field Programmable Gate Array (FPGA) are far more expensive - with respect to power consumption - than legacy SBCs (single board computers). The main contribution of the paper is a fair comparison (at the same clock frequency and with the same main CPU) of processing time and power consumption of two different boards used for deep neural network classification. We will highlight the relevance of classification speed with respect to common KPIs adopted to compare the performances of automatic classification such as Loss, Precision, Recall, etc. This will be particularly relevant in the challenging domains of hardware accelerated realtime control loops to provide distributed intelligence at the application level but also at the inner functions of emerging networking architectures.
The proceedings contain 38 papers. The special focus in this conference is on embedded Computer systems: Architectures, Modeling, and Simulation. The topics include: QCEDA: Using Quantum Computers for EDA;real-Ti...
ISBN:
(纸本)9783031783760
The proceedings contain 38 papers. The special focus in this conference is on embedded Computer systems: Architectures, Modeling, and Simulation. The topics include: QCEDA: Using Quantum Computers for EDA;real-time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for embedded Processing;A Novel System Simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator;Vitamin-V: Serverless Cloud computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your Defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing Techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced real-time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G systems: The Int5Gent Approach;RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software applications.
The proceedings contain 38 papers. The special focus in this conference is on embedded Computer systems: Architectures, Modeling, and Simulation. The topics include: QCEDA: Using Quantum Computers for EDA;real-Ti...
ISBN:
(纸本)9783031783791
The proceedings contain 38 papers. The special focus in this conference is on embedded Computer systems: Architectures, Modeling, and Simulation. The topics include: QCEDA: Using Quantum Computers for EDA;real-time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for embedded Processing;A Novel System Simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator;Vitamin-V: Serverless Cloud computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your Defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing Techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced real-time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G systems: The Int5Gent Approach;RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software applications.
Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach...
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For real-time edge computingapplications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. In order to minimize the communication delay between the IoT...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
For real-time edge computingapplications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. In order to minimize the communication delay between the IoT devices and the edge devices, we need a sophisticated approach for assignment IoT devices to the edge devices. Most of the heuristics solutions previously used to tackle the problem faced issues being solution stuck at local optima and high computational over head. To that end, researchers used reinforcement learning (RL) algorithms to explore the search space to get near optimal solutions. For our work, we consider RL based algorithms and show the preliminary results.
Functional verification problems such as clock domain crossing ( CDC), reset domain crossing (RDC), X-propagation and design for testability (DFT) readiness have been the mainstay of hardware verification flow for som...
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ISBN:
(纸本)9798350384406
Functional verification problems such as clock domain crossing ( CDC), reset domain crossing (RDC), X-propagation and design for testability (DFT) readiness have been the mainstay of hardware verification flow for some time now. With the ever-increasing chip complexity for modern-day mobile, multicore systems-on-chip (SoCs) with built-in advanced image processing, and scalable AI microcontrollers with billions of gates and numerous clock domains with ultralow device geometries, these problems are not only more significant nowadays, but need to be addressed very early in the design process for a timely tape out and first-pass Silicon success;failing which, there will be prohibitively expensive design bug fixes and costly design iterations. Static methods that perform search and analysis techniques to check for failures under all possible test modes, scenarios, and cases, have emerged as a very promising paradigm for solving these problems early, during RTL design. This paper describes a novel static verification, debug, and sign-off tool, Meridian DFT, that can detect specific design issues affecting testability at early RTL in presence of one or more test modes and provide ways to debug and quantify such issues. We present results on large scale (100+ million gates) industrial designs from mobile SoC and AI/Edge domain chips.
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