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If you are working on complex Artificial Intelligence (AI) or Machine Learning (ML) or Deep Learning (DL) designs using advanced node processes, you will understand the motivations for optimising CPU utilisation, device power and processing speed. Cutting-edge AI, ML & DL chips, by their very nature, are susceptible to intra-die process variability. Designers are often …
The concept of edge computing—putting low power servers close to the application instead of relying solely on “the cloud”--may not be a novel idea, but for 5G and the Internet of Things, adding AI capabilities to edge computing will be revolutionary. SoC designs will need to provide distributed intelligence in hardware to reduce energy use, …
Enabling the AI ecosystem with the only event dedicated to turning R&D into ROI INITIATOR IMPLEMENTOR OPTIMISER Whether you are a CxO looking to take your AI ambitions to the next level, or the Head of an R&D Center looking at the next big opportunity, The AI Summit Silicon Valley has you covered. The Initiate …
Whatever stage of your AI journey, we have the most relevant content for you with dedicated tracks. They are designed to provide our audience with the opportunity to dig even deeper into the adoption and implementation opportunities presented by AI and how they can deliver scalable AI strategies delivering real ROI. Initiate Track Driving value …
AI Business Week 2021 – Register Now! – On-Demand Thought Leadership with No Distractions AI Business week will take a unique look at the opportunities afforded by effective AI implementation across a number of sectors. Running Monday, February 22 – Thursday, February 25, each day will feature two 90-minute sessions presenting a unique look at opportunities …
Increase your machine learning model accuracy and performance with Intel-optimized XGBoost algorithms found in the Intel® AI Analytics Toolkit. Read More Gradient boosting has many real-world applications as a general-purpose machine learning technique for regression, classification, and page ranking problems. It’s a common choice for large problem sizes, yet training implementation of this method is …