{"id":108933,"date":"2020-06-20T15:03:30","date_gmt":"2020-06-20T22:03:30","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2020\/06\/quickly-embed-ai-into-your-projects-with-nvidias-jetson-nano"},"modified":"2020-06-20T15:03:30","modified_gmt":"2020-06-20T22:03:30","slug":"quickly-embed-ai-into-your-projects-with-nvidias-jetson-nano","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2020\/06\/quickly-embed-ai-into-your-projects-with-nvidias-jetson-nano","title":{"rendered":"Quickly Embed AI Into Your Projects With Nvidia\u2019s Jetson Nano"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/quickly-embed-ai-into-your-projects-with-nvidias-jetson-nano3.jpg\"><\/a><\/p>\n<p>When opportunity knocks, open the door: No one has taken heed of that adage like <a href=\"https:\/\/www.nvidia.com\/\">Nvidia<\/a>, which has transformed itself from a company focused on catering to the needs of video gamers to one at the heart of the artificial-intelligence revolution. In 2001, no one predicted that the <a href=\"https:\/\/spectrum.ieee.org\/tech-history\/silicon-revolution\/chip-hall-of-fame-nvidia-nv20\">same processor architecture<\/a> developed to draw realistic explosions in 3D would be just the thing to power a renaissance in deep learning. But when Nvidia realized that academics were gobbling up its graphics cards, it responded, supporting researchers with the launch of the <a href=\"https:\/\/developer.nvidia.com\/cuda-zone\">CUDA parallel computing<\/a> software framework in 2006.<\/p>\n<p>Since then, Nvidia has been a big player in the world of high-end embedded AI applications, where teams of highly trained (and paid) engineers have used its hardware for <a href=\"https:\/\/spectrum.ieee.org\/cars-that-think\/transportation\/self-driving\/nvidia-ceo-announces\">things like autonomous vehicles<\/a>. Now the company claims to be making it easy for even hobbyists to use embedded machine learning, with its US $100 <a href=\"https:\/\/developer.nvidia.com\/embedded\/jetson-nano-developer-kit\">Jetson Nano dev kit<\/a>, which was originally launched in early 2019 and rereleased this March with several upgrades. So, I set out to see just how easy it was: Could I, for example, quickly and cheaply make a camera that could recognize and track chosen objects?<\/p>\n<p>Embedded machine learning is evolving rapidly. In April 2019, Hands On looked at <a href=\"https:\/\/spectrum.ieee.org\/geek-life\/hands-on\/the-coral-dev-board-takes-googles-ai-to-the-edge\">Google\u2019s Coral Dev<\/a> AI board which incorporates the company\u2019s Edge <a href=\"https:\/\/en.wikipedia.org\/wiki\/Tensor_processing_unit\">tensor processing unit<\/a> (TPU), and in July 2019, <em>IEEE Spectrum<\/em> featured <a href=\"https:\/\/spectrum.ieee.org\/geek-life\/hands-on\/machine-learning-thats-light-enough-for-an-arduino\">Adafruit\u2019s software library<\/a>, which lets even a handheld game device do simple speech recognition. The Jetson Nano is closer to the Coral Dev board: With its 128 parallel processing cores, like the Coral, it\u2019s powerful enough to handle a real-time video feed, and both have Raspberry Pi\u2013style 40-pin GPIO connectors for driving external hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When opportunity knocks, open the door: No one has taken heed of that adage like Nvidia, which has transformed itself from a company focused on catering to the needs of video gamers to one at the heart of the artificial-intelligence revolution. In 2001, no one predicted that the same processor architecture developed to draw realistic [\u2026]<\/p>\n","protected":false},"author":396,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,1491],"tags":[],"class_list":["post-108933","post","type-post","status-publish","format-standard","hentry","category-robotics-ai","category-transportation"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/108933","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/396"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=108933"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/108933\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=108933"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=108933"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=108933"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}