STMicroelectronics enhances tool for ML applications development
NanoEdge AI Studio Version 3 is the first major upgrade of the software tool for machine-learning applications that ST acquired…
Edge AI accelerators are just sand without future-ready software
Unlike GPUs, which have established programming models and software toolchains, AI processors are confronted with lack in software support.
MCU’s machine learning quest at the edge aided by third parties
Renesas teaming up with MicroAI to train AI models directly on the MCU is an acknowledgement of the role of…
Why data preparation is crucial in AI workflows
Since data flows throughout the entire AI workflow, initial data preparation step is crucial as it ensures the most useful…
Implementing AIoT on MCUs
Implementation of AIoT on MCUs will increase exponentially in new applications as MCUs push the boundary on performance and blur…
Identifying AI security threats
Two group reports have recently been released regarding analyzing the problem of securing AI and the corresponding mitigations.
AI of Things (AIoT) explained
A look at the growing trend for intelligent connected devices enabling the AI of Things (AIoT), the technologies and some…
AI-based automatic visual packaging control
Here is how AI can be used to quickly and effectively identify and eliminate complex packaging quality problems in production…
Better Memristors Improve AI Operations
Neurone-like junctions made of mixed oxide-based materials could reduce the massive energy consumption of artificial intelligence operations.
Expanding tiny ML will speed the movement of AI into IoT devices
To reach its full potential, AI at the edge will need to be self-adaptive. Enter tiny machine learning.