We will introduce a multi-device synchronous debug & trace tool (released in September 2022) that enables synchronized execution, break control, and acquisition of trace information for multiple SoCs and MCUs.
Software is becoming the new sensor. This shift in thinking opens the door to incorporating more complex, AI-based algorithms, rather than just simple condition thresholds.
Relying only on high-level descriptive statistics rather than time and frequency domains will miss anomalies, fail to detect signatures and sacrifice value that an implementation could potentially deliver.
Real-time streaming data must be carved into smaller windows for consideration by a machine learning model, how that stream is carved up can have an impact on model performance and power consumption.
As sensor and MCU costs decreased, an ever-increasing number of organizations have attempted to exploit this by adding sensor-driven embedded AI to their products.
The more sophisticated machine learning tools that are optimized for signal problems and embedded deployment can cut months, or even years, from an R&D cycle.
This post offers tips on collecting data from high-sample-rate sensors for use with machine learning.
In this blog, we will present our work on solutions to facilitate the creation of remote development environments used in in-vehicle software development.
Efficient development using the security solution for Renesas' RL78 16-bit automotive microcontroller (Free sample software, Security Renesas Solution Starter Kit)!
We discuss the uniqueness of AI/ML tools when considering safety, give an overview of what are the AI/ML tools, how they contribute to ADAS/AD development, and explore the challenges in ensuring confidence in use of AI/ML software tools.