Tag: TinyML
Syntiant Brings AI Development to Everyone, Everywhere with Introduction of TinyML Platform
Tiny Machine Learning Development Board Now Available for Building Low-Power Voice, Audio and Sensor Applications using Edge Impulse’s Embedded ML Platform. Smallest Form Factor in the Market Fits Tiniest Devices; Delivers 20x More Throughput at 200x Less Energy Per Inference....
Continue ReadingSparkFun QuickLogic Thing Plus featuring EOS S3 MCU and eFPGA is now available at $45.95
SparkFun QuickLogic Thing Plus was originally crowdfunded on CrowdSupply that raised around $4500. If you did not back the product back then, no worries, you can buy it now on the SparkFun product page. SparkFun QuickLogic Thing Plus is a powerful FPGA board that comes with a...
Continue ReadingHow to Build a TinyML Smart Weather Station using Wio Terminal
You might have seen many ML-based projects for weather predictions. Where training of an ML model is followed by testing of the model for good accuracies. This tutorial will learn to deploy a similar type of ML model using an on-board TensorFlow Lite on the Wio Terminal. You will see...
Continue ReadingHimax WE-I Plus EVB: Computer vision and AI on the Edge
Edge computing has many great advantages. When you take some resource-heavy work from servers and let your less capable but still slightly powerful microcontrollers, you end up freeing server resources. Also, depending on what you are doing, you can get away with not using a server at...
Continue ReadingEta Compute’s ECM3532 AI Sensor Board with TENSAI SoC for TinyML
Eta Compute’s ECM3532 AI Sensor board is a low-power AI development platform with advanced sensors, compatible with sound classification, keyword spotting, activity classification, context awareness, and defect detection. It has an embedded Cortex-M3 microcontroller that functions up...
Continue ReadingTinyML Enables AI in Smallest Endpoint Devices
"TinyML is proof that good things come in small packages", or so does ARM describe it, as it promises with TinyML to change a different approach, by running optimized machine learning models on small and efficient microcontroller-based endpoint devices, instead of bulky, power-hungry...
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