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…
Training an algorithm to detect and prevent early blindness
This article provides a walkthrough of the steps needed to quickly train an algorithm to detect diabetic retinopathy and prevent…
Renesas and Syntiant develop voice-controlled multimodal AI solution
The new offering enables low-power voice-controlled operation of embedded vision AI systems in IoT and edge applications.
Manage control plane security in real-time using network AI
The implementation of AI techniques by the trusted control/compute unit (TCU) can greatly reduce the frequency and severity of attacks.
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.
TOPS: The truth behind a deep learning lie
A formula for determining the real-world performance of AI and others chips when running deep learning workloads.
A multi-tier approach to machine learning at the edge
A multi-tier approach to machine learning at the edge can help streamline both development and deployment for the AIoT.
MCU suppliers increase memory content to support AIoT designs
High-resolution displays and complex AI datasets in IoT endpoints and wearable designs increasingly demand additional memory content.