Tag: TinyML

Edge Impulse, a company that specializes in machine learning, has recently made an announcement regarding a partnership with Seeed Studio. The purpose of this partnership is to add support for the Grove Vision AI Module to Edge Impulse Studio as well as the Edge Impulse software...

In a world where voice assistants can be activated by just a simple activation voice command line like “Ok Google” or “Alexa”, the importance of vocal input is crucial. Nicla Voice is a development board designed to help developers create and deploy machine learning models on...

Sipeed released two embedded devices based on Bouffalo Lab's RISC-V BL616 microcontroller. The M0S module has interfaces for WiFi 6, Bluetooth 5.2, and Zigbee. It also works with DVP cameras, RGB LCDs, and Ethernet RMII. On the product page, it says that the BL616(RV32GCP) has a...

Polyn Technology has been providing tinyML solutions with its NASP technology, a neuromorphic analog signal processing, ideal for next-gen sensor-level devices in real-time edge AI applications. The company has now announced the availability of its NeuroVoice AI solution for on-chip...

Edge Impulse has announced support for Arduino's compact Nicla Sense ME board targeted at Edge AI Motion and Environment projects -- a new standard for intelligent sensing solutions. The edge AI and tinyML expert promised to give full support for the device's newly integrated...

There have been several recent developments around TinyML and edge AI applications, and to supply the increasing demand from the developer community, many embedded electronic device manufacturers around the world are designing AI development boards. Beijing-based online embedded...

Computer vision applications that range from object detection and pattern recognition to computational healthcare and security surveillance systems take the input image for further processing, which in traditional hardware implementation has a vision sensing and vision processing...

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...