{"id":41784,"date":"2021-04-02T16:00:21","date_gmt":"2021-04-02T08:00:21","guid":{"rendered":"\/blog\/?p=41784"},"modified":"2024-06-19T08:12:05","modified_gmt":"2024-06-19T08:12:05","slug":"how-to-choose-hardware-for-edge-ml","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2021\/04\/02\/how-to-choose-hardware-for-edge-ml\/","title":{"rendered":"How to Choose Hardware for Edge ML!"},"content":{"rendered":"\n

It\u2019s almost common knowledge that machine learning requires more computational power than your average day-to-day tasks. With a variety of offerings from Google, NVIDIA, Intel and others that range from TPUs, tensor cores to GPUs, it\u2019s becoming increasingly difficult to choose hardware for Edge ML tasks. In this article, I aim to shed some light on the technologies that are available on the market and your options when it comes to hardware for both machine learning training and inferencing on the edge. Let’s go!<\/p>\n\n\n\n

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What goes on behind Machine Learning?<\/strong><\/h2>\n\n\n\n
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Machine learning is a broad field that has seen tremendous progress in the recent years. It is based on the principle that a computer can autonomously improve its own performance on a given task by learning from data – sometimes even beyond the capabilities of humans.<\/p>\n\n\n\n

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Today, machine learning can perform many advanced tasks, including but not limited to:<\/p>\n\n\n\n