Tag: TinyML

Aspinity's analogML core features the improved capabilities of a tinyML chip with low-power analog neuromorphic computing architecture– with a system-level approach to low-power edge processing. Without making use of power-hungry digitization and digital processors, the analogML core...

Recent breakthroughs in embedded machine learning have increased the demand for TinyML applications. In dealing with TinyML, there is an excellent platform to get started and build complex projects, Edge Impulse. We have also witnessed several new embedded devices coming to market...

AONDevices, Inc., a provider of ultra-low power, high-performance processors for a new era of edge AI, today announced that engineering samples of its new AON1100™ processor are now available. The AON1100 Edge AI processor enables unprecedented sensing capabilities in...

RAKWwireless, a Chinese embedded device manufacturer known for its rich IoT ecosystem with several sensors’ boards and add-on modules for existing baseboards, launched a series of new modules with special highlights on the WisBlock LPWAN module and WisBlock baseboard. The reason for...

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....

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...

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...

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...