Vertex.AI brings the full power of deep learning to every device, big or small. We add visual intelligence capabilities to edge devices with our Vision framework and deliver advanced deep learning capabilities to any device with our fully supported Vantage platform.
Do you have a uniquely challenging machine vision problem or deployment scenario? Our experts are here to help.
Apply state of the art deep neural networks to detect, classify, track, and identify objects and other visual elements in real time. Building on Vantage, Vision is compatible with nearly any device and operating system. Batteries included, PhD optional.
Everything you need to deploy deep learning to fit your unique requirements. Vantage provides a fully supported software platform that unlocks the deep learning capability of chips from low-power mobile processors to full-size GPUs, all while maintaining compatibility with popular open source tools.
Benchmarking Deep Neural Nets for Real-Time Vision — Aug 29, 2017
Recently we posted early results from our work to bring deep learning to more people through OpenCL support including initial benchmarks on AMD and NVIDIA hardware. As a business we are building on this technology to bring real-time computer vision to every device. In this post we will discuss the key issue of processing speed, open source a tool we use to measure speed on real workloads, and share our performance progress. Through careful optimization our runtime software, code-named Plaid, is now up to 1.4x faster than TensorFlow 1.3 + cuDNN 6 for real-time vision tasks.
Open Source Deep Learning on AMD and Beyond — Aug 17, 2017
Earlier this week, we posted a first look at our work to bring deep learning to more people on more platforms. Today, we’re adding details on our plan to open source our software and an update on our development progress. With our support for the OpenCL open standard, people with a GPU from any manufacturer, including NVIDIA, AMD, and Intel, will soon be able to get started with real datasets in minutes. Users won’t need to sacrifice speed for that freedom, our software is as fast as TensorFlow + cuDNN in some cases and it will continue to improve.
Bringing Deep Learning to OpenCL — Aug 14, 2017
I'm excited to announce Vertex.AI's work to bring deep learning to OpenCL and share a first look at our results so far. This work is intended to make deep learning accessible to more people and speed up progress across the field. Read on for the details.
Our overarching goal is to bring intelligence to more devices. Today, deep learning research is showing new and powerful accuracy in areas like image understanding, speech recognition, language translation, and more. At the same time, to get the necessary computations running on most chips requires rare expertise and substantial software development effort. We are addressing that problem at two layers: First by making it possible with Vantage to run deep neural nets on a wide variety of chips, and second by building on top of that our Vision ready-to-use toolbox of pre-packaged visual intelligence capabilities. We support each with hands-on support and training to ensure your challenge is solved.