industry news
Subscribe Now

STMicroelectronics extends embedded AI solutions with advanced features to ease Machine-Learning development

Geneva, November 10, 2022 – Enhancing its tools to accelerate embedded Artificial-Intelligence (AI) and Machine-Learning (ML) development projects, STMicroelectronics has released upgrades to both NanoEdge AI Studio and STM32Cube.AI. These tools facilitate moving AI and ML to the edge of an application. At the edge, AI/ML delivers substantial advantages, which include privacy by design, deterministic and real-time response, greater reliability, and lower power consumption.

NanoEdge AI Studio is an automated ML tool for applications that do not require the development of neural networks. It is used with STM32 microcontrollers (MCUs) and MEMS sensors that include ST’s unique embedded intelligent sensor processing unit (ISPU). For developers needing to use neural networks, STM32Cube.AI is an AI model optimizer and compiler for STM32. The two new releases deliver features that help design and implement high-performance AI/ML solutions quickly and with minimum investment.

NanoEdge AI Studio version 3.2 now contains an automatic datalogger generator that increases development productivity. Its inputs include the ST development board and developer-defined sensor parameters, such as data rate, range, sample size, and number of axes. With these, NanoEdge AI Studio produces the binary for the development board without the developer having to write any code.

Because dataset quality directly impacts machine learning performance, the new data-manipulation features in NanoEdge AI Studio allow the user to clean and optimize the captured data in the NanoEdge AI Studio in a few clicks. A new validation stage has also been added, which helps users assess their algorithms by showing inference time, memory usage, and common performance metrics such as the accuracy, and F1-Score. It also highlights more information about the pre-processing and ML model involved in the selected library. The newest enhancement to the NanoEdge AI Studio adds more pre-processing techniques and ML models for anomaly detection and regression algorithms that boost performance. In addition, the tool supports creation of smart libraries that can predict future system states using multi-order regression models.

STM32Cube.AI version 7.3 is an essential tool for developing cutting-edge AI/ML solutions. Fully integrated into the STM32 ecosystem, it enables conversion of pretrained neural networks into optimized C code for the industry’s most popular family of 32-bit Arm® Cortex®-core MCUs. The enhanced STM32Cube.AI adds greater flexibility for neural-network (NN) optimizations. The tool can adapt existing neural networks to achieve performance demands, fit within memory limitations, or, in a balanced optimization, get the best of both. The update also brings support for TensorFlow 2.10 models and new kernel performance improvements.

For more information about AI/ML solutions from STMicroelectronics and to discover real-life use cases, please visit https://stm32ai.st.com/.

Leave a Reply

featured blogs
Nov 23, 2022
The current challenge in custom/mixed-signal design is to have a fast and silicon-accurate methodology. In this blog series, we are exploring the Custom IC Design Flow and Methodology stages. This methodology directly addresses the primary challenge of predictability in creat...
Nov 22, 2022
Learn how analog and mixed-signal (AMS) verification technology, which we developed as part of DARPA's POSH and ERI programs, emulates analog designs. The post What's Driving the World's First Analog and Mixed-Signal Emulation Technology? appeared first on From Silicon To So...
Nov 21, 2022
By Hossam Sarhan With the growing complexity of system-on-chip designs and technology scaling, multiple power domains are needed to optimize… ...
Nov 18, 2022
This bodacious beauty is better equipped than my car, with 360-degree collision avoidance sensors, party lights, and a backup camera, to name but a few....

featured video

Unique AMS Emulation Technology

Sponsored by Synopsys

Learn about Synopsys' collaboration with DARPA and other partners to develop a one-of-a-kind, high-performance AMS silicon verification capability. Please watch the video interview or read it online.

Read the interview online:

featured paper

Algorithm Verification with FPGAs and ASICs

Sponsored by MathWorks

Developing new FPGA and ASIC designs involves implementing new algorithms, which presents challenges for verification for algorithm developers, hardware designers, and verification engineers. This eBook explores different aspects of hardware design verification and how you can use MATLAB and Simulink to reduce development effort and improve the quality of end products.

Click here to read more

featured chalk talk

HARTING's HAN® 1A Connector Series

Sponsored by Mouser Electronics and HARTING

There is a big push in the electronics industry today to make our designs smaller and more modular. One way we can help solve these design challenges is with the choice of connector we select for our designs. In this episode of Chalk Talk, Goda Inokaityte from HARTING and Amelia Dalton examine the role that miniaturized connectivity plays in the future of electronic design. They also how HARTING's Han 1A connectors can help reduce errors in installation, improve serviceability and increase modularity in your next design.

Click here for more information about HARTING Han® 1A Heavy Duty Power Connectors