Xilinx Kria System-on-Modules (SOMs) provide a secure, production-ready multi-core Arm and FPGA platform, including memories, power management, and Yocto or Ubuntu Linux to build accelerated AI-enabled applications at the edge. Kria SOMs enable smart vision applications across cities, factories, and […]
When developing a new edge AI solution, you want to focus on your system’s unique functionality. In this session, we will share the different foundational developer kits available from Intel’s partners to help speed your edge AI solution development. These […]
The ability to perform neural network inference in resource-constrained devices is fueling the growth of machine learning at the edge. But application solutions require more than just inference—they also incorporate aggregation and pre-processing of input data, and post-processing of inference […]
Computer-vision-enabled cameras have demonstrated the potential to bring compelling functionality to numerous applications. But to realize the full potential and assist with the growth of AI-enabled cameras, we must drastically simplify the work of developing, deploying and maintaining these cameras. […]
Many applications, such as medical imaging, lack the large amounts of data required for training popular CNNs to achieve sufficient accuracy. Often, these same applications suffer from an imbalanced class distribution problem that negatively impacts model accuracy. In this talk, […]
We started with a simple vision in mind: “How might we get the best deep learning performance with real-world solutions deployed into production?” In this session, the lead architect of Intel Distribution of OpenVINO toolkit, Yury Gorbachev, will discuss the […]
Achieving high performance and power efficiency for machine learning inference at the edge requires maintaining high chip utilization, even with a batch size of one, while processing high-resolution image data. In this Over-the-Shoulder tutorial session, we will show how Edgecortix’s […]
Today, TinyML focuses primarily on shoehorning neural networks onto microcontrollers or small CPUs but misses the opportunity to transform all of ML because of two unfortunate assumptions: first, that tiny models must make significant performance and accuracy compromises to fit […]
The number and resolution of image sensors continue to increase in mobile, AR/VR, automotive, drone, mobile, and robotics platforms, which also use a mix of other types of sensors in addition to image sensors. Getting the most from these sensors […]
There are many large-scale opportunities for edge AI solutions across a broad range of Internet of Things applications. But deploying the kinds of complete solutions that customers desire—at scale—is challenging. Fragmentation in the IoT space means that there is huge […]
