Tag: Machine learning

Arduino designed Nano 33 BLE Sense Rev2 board for beginners and entry-level professionals to get familiar with embedded machine learning capabilities. The Arduino Nano 33 BLE Sense Rev2 features Nordic Semiconductor’s nRF52840 microcontroller featuring an Arm Cortex-M4 32-bit...

In today's world, data is everywhere, and the need for data analysis is increasing rapidly. One of the most promising technologies to enable an intelligent analysis of data is Machine Learning (ML). Machine Learning has revolutionized many industries by helping them make sense of the...

A UK-based startup that provides silicon and software IP, Imagination Technologies has partnered with Baidu PaddlePaddle, a provider of artificial intelligence solutions, to co-develop an open-source machine learning library for Model Zoo. The new AI model will cover several AI...

SparkFun, a Colorado-based embedded electronic device manufacturer, has announced the release of the Machine Learning and AI Concept Kit, which serves as a starting point for learning the fundamentals of prediction algorithms based on historical data and inputs. With the help of this...

The global semiconductor giant STMicroelectronics has simplified interaction with trained devices with its new inertial sensor ISM330ISN featuring an Intelligent Sensor Processing Unit (ISPU). ISPU is essentially a low-power, high-performance programmable core with high computational...

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...

If you are working on edge-AI devices then you would’ve considered buying NVIDIA Jetson Xavier NX, which is a best-in-class deep learning-accelerating module claimed to be the world's smallest supercomputer that was released last year. With the increasing implementation of AI-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...