Steven Tateosian’s Post

In the huge tides of LLMs, edge computing remains relevant. ARM has released a new chip. https://lnkd.in/gs4PX_Na I am very sure that a large number of applications will get benefitted because of this. Having increased computing power on edge devices reduces data transfer issues, and provides local insights (cost and performance improvement)! #edgeai #edgecomputing #artificialintelligence #machinelearning

This study tackles this high simulation time problem by proposing a #generalized #regression #neural #network (#GRNN) based machine learning (ML) approach to predict the k-coverage performance of a #wireless #multihop #networks (#WMNs) placed in a #rectangular-#shaped #region (#RSR). To train the GRNN algorithm for two different set-ups, i.e., without and with #boundary #effects (#BEs), the authors extract six potential features, namely length of RSR, breadth of RSR, sensing range of sensor nodes (SNs), number of sensor nodes (SNs), standard deviation of SEs (σ), and the value required k through simulations. They also evaluate the importance of individual features utilizing the regression tree ensemble technique and simultaneously analyze the sensitivity of each feature to predict the k-coverage probability of the network. ---- Dr. Jaiprakash Nagar More details can be found at this link: https://lnkd.in/gfaJRUcx

If you missed our last #tinyMLTalks webcast series: Making ML Tiny with High-Level Synthesis with Russell Klein, Technical Director of Siemens EDA, we have you covered! The video is now available on tinyML YouTube channel: https://lnkd.in/gtvKQJPf  This talk showed how High-Level Synthesis can be used to quickly create and assess multiple RTL implementations for an AI accelerator from a single algorithmic description. Russell explained how HLS can be used to find the optimal quantization for features and weights, layer-by-layer or globally for an entire network. And we showed how HLS can be used to investigate caching strategies and their impact on power and performance. High-Level Synthesis can be the key to deploying ML into the most constrained and challenging Edge and IoT systems. Danilo Pietro Pau Evgeni Gousev Pete Bernard Olga Goremichina Mathilde Karsenti #ml #tinyml #artificialintelligence #machinelearning #ai #aiforgood #aiforall

Edge Computing in Machine Learning Our first Rapid Read in this domain with the basics of Edge Computing, its use, and challenges. Follow us for more such blogs from Dishant Parikh and team AV DEVS solutions https://lnkd.in/gxrRJNW3 #TechInsights #Edgecomputing #machinelearning

As we delve further into our AI and ML offerings, it's crucial to tackle real-world challenges head-on. Our recent implementation of Edge Computing has delivered an enterprise-level solution primed for global deployment. Here, we present a quick overview of this captivating technology use case.

SUNRISE-6G proudly announces a significant #milestone in edge computing and #machinelearning. The project's #publication, "Unlocking the Path Towards Automation of Tiny Machine Learning for Edge Computing", presents #innovative advances in automated aspects of #TinyML. Accepted and presented at the esteemed SmartNets2024 conference co-sponsored by IEEE and IEEE Communications Society, which took place from 28 to 30 May 2024, and presented the innovative advances made by the #SUNRISE6G team. For more information, visit https://lnkd.in/e-vrv8g9 Georgios Samaras, Vasileios Theodorou, PhD, Marinela Mertiri, Theodoros Bozios, Intracom Telecom, Christos Verikoukis, Smart Networks and Services Joint Undertaking (SNS JU) 6G Smart Networks and Services Industry Association European Commission, Dr. Odysseas Pyrovolakis, Pavlos Fournogerakis. #research #6GIA #6GSNS #IEEE #innovation

Designed to be used with MPUs such as NXP's i.MX family of applications processors , the GenAI solutions are said to make it easier to deploy intelligence at the edge by training large language models (LLMs) on specific contextual data

In 2024, innovative ways to enhance SIM Internet bonding include leveraging AI for dynamic network management, using advanced algorithms for better load balancing, and integrating 5G and 6G technologies for ultra-fast speeds. #SIMBonding #TechInnovation 🚀📶 Cloud-based bonding solutions can optimize performance by analyzing real-time data, while edge computing can reduce latency. Enhanced security protocols will protect data integrity, making SIM bonding more reliable and efficient for various applications. #AI #5G #EdgeComputing 🌐🔒

What are VPUs in context of edge technologies❓ VPUs((Vision Processing Units)) in edge technologies are integral to enhancing the capabilities of edge devices with efficient, powerful, and real-time visual data processing, which is essential for a wide array of modern technological applications. They act as AI accelerators by preprocessing visual data before it is input into models. The focus is typically on processors that are specifically designed to handle and accelerate tasks related to computer vision directly on edge devices. 📌 Key aspects of VPUs in edge technologies include: 1️⃣ Real-time Processing 2️⃣ Reduced Latency(As latency is global problem of edge technology) 3️⃣ Enhanced Privacy and Security(As privacy is the main purpose of edges) 4️⃣ Scalability and Flexibility Explore More ⬇ https://shorturl.at/guwyX #ArtificialIntelligence #ComputerVision #EdgeComputing

If you missed #tinyAI Forum on Generative AI on the Edge last week, we have you covered! The video recording of Jose Angel Miranda Calero, Post-doc research assistant, Embedded Systems Laboratory, EPFL with presentation name: "GenAI and the Transformation of Edge Computing: Leveraging Heterogeneous Circuits for Innovation" is now available on tinyML YouTube channel https://lnkd.in/gG5p52qy This presentation explored the forefront of GenAI applications and their deployment in embedded systems. The challenge of incorporating GenAI into these systems is significant, as it involves local processing and generation of complex data within the constraints of limited computational resources and energy efficiency. To overcome these challenges, the development of specialized accelerators and the adoption of open-source frameworks are critical. A notable solution in this space involves the use of advanced, heterogeneous integrated circuits, which facilitate the rapid development of flexible and reconfigurable solutions. Such systems, characterized by their versatile processing units, offer the necessary balance between adaptability and computational power essential for GenAI tasks. Illustrative examples, including wearable health monitors providing instant diagnostic feedback, demonstrate the practicality of embedding GenAI in edge devices with appropriate strategies. This approach, focusing on the optimization of these sophisticated integrated circuits, outlines a method to navigate beyond the limitations of existing systems, thereby expanding the potential of GenAI in our daily lives. See you at our annual tinyML Summit, April 22-24. Register now https://lnkd.in/g46W-pwD #tinyml #ml #artificialintelligence #genai #generativeai #machinelearning #ai Davis Sawyer Evgeni Gousev Olga Goremichina Tinoosh Mohsenin Danilo Pietro Pau Max Petrenko Gian Marco Iodice Daniel Situnayake