Intel Ignite’s Post

I am glad to have participated in the recent review of the research project Mannheim-CeCaS (CentralCarServer) at TU München by presenting my part in the AI-accelerator work package with a coordinating role. The aim of the project is to develop an #automotive #supercomputing platform and AI acceleration can assist in ADAS and Autonomous Driving use cases. Different AI approaches are required to tackle the complexity of the applications, from server-side solutions to near-sensor edge computing and low-power neuromorphic computing, which was also part of our work package. Τhe discussion that followed highlighted the significance of the HW/SW Co-Design, a Toolchain for the optimal use of hardware, and the Reliability Analysis as part of the safety and security subject that is crucial for automotive electronics. #CeCaS #autonomousdriving #ai #ADAS

Researchers have made a machine-learning model for high-resolution computer vision. This model could allow vision apps that require a lot of computing power to run on edge devices, such as autonomous driving or medical image segmentation. The researchers made a hardware-friendly architecture so it could be easy to run on different devices, like virtual reality headsets or edge computers on autonomous vehicles. Read the complete article here: https://lnkd.in/gj-Kd-tc Ministry of Electronics and Information Technology Digital India Programme INDIAai nasscom Sushil Kumar Jangid Kavita Bhatia Abhishek Singh Massachusetts Institute of Technology

Researchers create a compact, all-optical device with the lowest microwave noise ever achieved for an integrated chip. In a new Nature study, Columbia Engineering researchers have built a photonic chip that can produce high-quality, ultra-low-noise microwave signals using only a single laser. The compact device — a chip so small, it could fit on a sharp pencil point — results in the lowest microwave noise ever observed in an integrated photonics platform. The achievement provides a promising pathway towards small-footprint ultra-low-noise microwave generation for applications such as high-speed communication, atomic clocks, and autonomous vehicles.

𝗣𝗮𝗻𝗲𝗹 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁📢 𝗦𝗮𝗳𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗗𝗿𝗶𝘃𝗶𝗻𝗴: 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗼𝗿 𝗙𝗶𝗰𝘁𝗶𝗼𝗻? The use of AI in technology development has been a hot topic around the world🔎🌍 WMG is hosting a panel discussion at this year's ADAS & Autonomous Vehicle Technology Expo to discuss 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗗𝗿𝗶𝘃𝗶𝗻𝗴, 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁 𝘀𝗮𝗳𝗲𝗹𝘆 𝗮𝗻𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝘆, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱 𝗳𝗼𝗿 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘀𝗽𝗮𝗰𝗲📄 We are very honoured to have the following panellists from the United Nations Economic Commission for Europe and leading technology developers join this discussion🤝 Richard Damm, President, @KBA and Chair of GRVA, United Nations Economic Commission for Europe Prof Siddartha Khastgir, Head of Safe Autonomy, WMG, University of Warwick Dr Sarah Gates, Director of Public Policy, Wayve  Matteo Oldoni, Senior Manager, AI and AD, NVIDIA  Dr Georg Stettinger, Senior Project Manager, R&D Funding, Infineon Technologies 🚨𝗣𝗮𝗻𝗲𝗹 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗱𝗲𝘁𝗮𝗶𝗹𝘀: 𝗗𝗮𝘁𝗲: 4 June 2024 𝗧𝗶𝗺𝗲: 14:25 CET 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Messe Stuttgart, Germany Come and have a conversation with us! #AI #SafeAI #AISafety #AutomatedVehicles #AutomatedDriving

🚗 Exciting News! 🚗 I am thrilled to announce the publication of our latest paper titled "5G on the Roads: Latency-optimized Federated Analytics in the Vehicular Edge" in IEEE Access. As the automotive industry is moving toward an era of autonomous driving, coordination among vehicular actors becomes paramount. Our research focuses on addressing the critical challenge of latency in information gathering, processing, and redistribution within high-mobility automotive environments. In this paper, we present a privacy-preserving system for collecting and sharing data in vehicular networks, specifically focusing on HD maps. Leveraging the power of federated analytics, we've designed a low-latency, scalable processing, and data distribution framework that capitalizes on the capabilities of the 5G edge. By strategically combining mobile vehicular nodes and static nodes, we have unlocked new dimensions of efficiency and safety for autonomous vehicles. I would like to extend my heartfelt gratitude to my esteemed co-authors: - Laszlo Toka - Márk Konrád - István Pelle - Balázs Sonkoly - Marcell Szabó - Bhavishya Sharma - Madhuri A. - Sree Teja Deekshitula - Antony Franklin This work is a collaborative effort of NEWS Lab, Indian Institute of Technology Hyderabad, India, and HSN Lab, Budapest University of Technology and Economics. The full paper can be accessed here: https://lnkd.in/dz4-Q3xR I am immensely proud of the team's hard work and dedication, and I am confident that our research will significantly impact the future of autonomous driving technology. 🌐🚀  #FederatedLearning #AutonomousDriving #5GTechnology #VehicularEdge #ResearchInnovation

⚡Anyverse and Sony Semiconductor are collaborating to ensure sensor and AI model seamless integration to accelerate ADAS deployment Thanks to Anyverse synthetic data solution incorporating Sony Image Sensor Models, perception teams using or planning on using those sensors can now generate highly accurate synthetic datasets and physically-based sensor simulation to evaluate and test their Advanced Driver Assistance System (ADAS) and Automated Driving AI models. 🟢Learn more https://lnkd.in/dDZrEWun #Automotive #ADAS #AutonomousDriving #Sony #SensorSimulation #ImageSensors #MachineVision

AVCC and MLCommons Reveal Industry Benchmark for Automotive Perception with Cognata Synthetic Dataset! Cognata is proud to announce our significant contribution to the groundbreaking MLPerf Automotive Benchmark Proof-of-Concept (POC) released by MLCommons and the Autonomous Vehicle Computing Consortium, Inc. (AVCC). This release marks a crucial milestone in developing a comprehensive benchmark suite for vehicle AI systems. Check the video: running the SSD-ResNet50 Object Detection model at 8 megapixel image input. This model has been trained using a large synthetic dataset from Cognata, rendered with 8 megapixel resolution. With this sophisticated training regimen, the SSD-ResNet50 model excels in identifying specific road objects which are not just recognized but also accurately labeled and highlighted within precise bounding boxes. Read the full press release: https://lnkd.in/gqGuAVnn Alon Atsmon Danny Atsmon Shay Rootman Nofar Assis Gadi Sznajder Omri Reftov Arm Bosch NVIDIA ABTF Automotive Business Task Force cKnowledge Marvell Technology QUALCOMM TECHNOLOGIES INTERNATIONAL, LTD. #syntheticdata #autonomousdriving #digitaltwin #photorealistic #machinelearning #autonomousvehicle #automotiveindustry #AutomotiveInnovation #VehicleSimulation

With the integration of advanced quantum materials, we're making significant strides in reducing the cost of Short-Wave Infrared (SWIR) sensors, ultimately democratizing their accessibility across industries.🛠️ Imaging applications are evolving from their traditional role of capturing 'pretty pictures' to becoming core sensing tools. Incorporating wide-spectrum capabilities brings multifaceted advantages in terms of performance and functionality in computer vision-first applications.📷 We take immense pride in our involvement in the #AUTOVISION Spearhead project that now ends and was aimed to develop low-cost SWIR image sensors and evaluate them under adverse weather conditions. These image sensors can be a key component for the development of the next wave of advanced driver assistance systems and the way towards the wider adoption of semi and fully autonomous vehicles.🚗 Thank you to our industrial partners on this project imec, AIXTRON SE, and Veoneer. We would also like to acknowledge Graphene Flagship for supporting this project.🤝 Together, we're shaping a future where machines perceive and interpret their surroundings with unprecedented intelligence.💡 #Qurv #SWIRSensors #IntelligenceThroughVision #GrapheneFlagship Want to learn more about Qurv? Write to us: [email protected]  Visit us: www.qurv.tech