Новини світу мікро- та наноелектроніки

HighTec EDV-Systeme and Stellar SR6: the right compiler is as important as the right MCU

ELE Times - Fri, 12/08/2023 - 07:23

Author: STMicroelectronics

Do teams take as much care and effort in choosing their compiler as they do their microcontroller? It’s such a critical question that we work closely with HighTech EDV-Systeme GmbH, a member of the ST Partner Program and the largest commercial open-source compiler vendor. The company, which focuses on automotive applications, announced in 2022 supporting our latest Stellar SR6x integration microcontrollers on top of the existing compatibility with our SPC5x devices. It was the first time the HighTec compiler supported one of our automotive-grade microcontrollers with a Cortex core. Let us thus explore why it is a milestone and why choosing the right compiler is as important as selecting the right processor.

The importance of the close partnership between compiler vendor and silicon maker What were the first fruits of this partnership?

The collaboration between ST and HighTec dates back to 2011. Automotive module makers have had to deal with numerous systems and manufacturers, which means that using one compiler for many projects is essential. Concretely, the HighTec compiler allowed short build times and fast execution speed of the application in conjunction with the most stringent requirements of the ISO 26262 standard. And as we worked with HighTec, module makers adopted our SPC devices without drastically changing their workflows. Additionally, our close collaboration enabled our customers to take advantage of the features we provided on our hardware platform more rapidly. In a nutshell, beyond supporting ST devices, HighTec helps developers with optimized run-time performance and build times.

What to look for in a compiler vendor?

Looking at the historical context of our partnership with HighTec, it is easy to understand why a close relationship between the silicon and compiler vendors is essential. The right compiler is one that reflects a collaboration with chip makers by receiving frequent updates, compatibility with new devices, and significant performance optimizations. This close interaction also led the ST Authorized Partner to recommend our devices when providing consulting services. As we work with HighTec to ensure they have access to the right expertise and documentation, the company can better serve its community by providing the best recommendations to its customers.

The foundational quality of the hardware platform Why did we create the Stellar SR6? The Stellar SR6 supported by HighTecThe Stellar SR6 supported by HighTec

The partnership between ST and HighTec explains why the compiler is now compatible with Stellar SR6 devices. Thanks to their Cortex-R52+ cores, the ST Integration MCUs provide real-time and deterministic performances. As the name implies, they aim to integrate modules into one platform, which explains why they share interfaces, such as Ethernet, CAN-FD, CAN-XL, or LIN, and why to run multiple virtual machines on them. The SR6G (also called Stellar G) targets zone controllers, gateways, and body integration by creating a hub managing the I/Os, data, and power consumption. The Stellar P (SR6P) devices, on the other hand, help simplify designs by integrating drivetrains and domain-oriented applications onto one platform.

How do the Stellar SR6 drive innovation?

The Stellar SR6 devices are highly symbolic because the philosophy behind their specification reflects the new trends in software-driven vehicles. For instance, the SR6G7 was recently a highlight of the prestigious VLSI 2023 conference thanks to its embedded phase change memory that nearly doubles in capacity when in full over-the-air mode. Thanks to years of R&D, ST was able to show how car makers could more easily and cost-effectively implement over-the-air updates. Hence, ST’s line of Integration microcontrollers aims to solve some of the development challenges inherent to the new applications powering vehicles.

HighTec and the critical building blocks of its software ecosystem What does a flexible and pragmatic ecosystem look like?

When HighTec announced supporting our Stellar SR6 devices, the company made a point to emphasize compiler support and performance optimization. Indeed, our teams worked closely together to ensure that developers could more easily take advantage of our hardware features when using HighTec’s ecosystem. Whether programmers want to use our encryption capabilities, a hardware security module (HSM), the new OTA functionalities, or ST’s software components, such as MCAL, or the Safety and Security packs, HighTec not only baked-in support but acquired expertise to further its consulting services.

How have past and present decisions impacted the future?

Behind the scenes, the rapid adoption of our Cortex devices was possible because of technical choices HighTec made decades ago. Indeed, early in the 1990s, the company prioritized open-source compilers, which was unusual then, especially for a vendor in the automotive industry. The company first settled on GCC and then moved to LLVM around 2015 due to licensing reasons and because there was a clear trend within the industry. Consequently, the open-source aspect inspired developers looking to abandon proprietary solutions, while LLVM ensured the company could rapidly support ARM architectures.

Today, ST and HighTec work on new applications in EVs that have unique requirements. For instance, MCUs like the Stellar SR6 have a general timer module (GTM) that developers can program in C to generate I/O signals and thus offload the main CPU. These GTMs are essential when driving silicon carbide (SiC) transistors, which are increasingly necessary to improve efficiency and battery lifeHowever, the high frequency needed to drive these new devices means developers must create a high-performance code with performance similar to assembly and thus require extreme optimizations at the compiler level. Put simply, choosing the right compiler is crucial not only for today’s applications but tomorrow’s as well.

Read the full article at https://blog.st.com/hightec-stellar-sr6/

The post HighTec EDV-Systeme and Stellar SR6: the right compiler is as important as the right MCU appeared first on ELE Times.

Smartphone production rebounds by 13% in Q3, after eight quarters of year-on-year declines

Semiconductor today - Thu, 12/07/2023 - 20:40
Fueled by reduced channel inventories and spikes in seasonal demand, global smartphone production rose by 13% quarter-to-quarter to about 308 million units in third-quarter 2023, according to market research firm TrendForce. Although this figure has yet to reach pre-pandemic levels, it is up 6.4% year-on-year, ending an eight-quarter streak of annual declines...

Exploring software-defined radio (without the annoying RF) – Part 1

EDN Network - Thu, 12/07/2023 - 17:16

I needed to come up with a communications device to transmit a handful of bytes, every hour, from a small off-grid solar system to my shop about 150 feet away. The first thought was Wi-Fi, but I already have many dozens of devices on my Wi-Fi and keeping them all working is like spinning plates (for those under 50 see this video). I wanted a different solution and thought of using ultrasonic transducers configured in a software-defined radio (SDR) type framework as the transceiver. While working on the design I realized that it would be very useful for anyone that wants to explore, or teach about, the physical (PHY) layer of the SDR. (The PHY layer of the firmware takes the received signal and demodulates it, slices it, checks it, and sends the data packet off for further processing on the next layer of the OSI model. On the transmit end, it builds a modulated signal based on the data to be sent, the baud rate, and the modulation scheme selected, then sends the modulated signal to a transmitter.)

Wow the engineering world with your unique design: Design Ideas Submission Guide

Why use ultrasonics

As you will see, by using ultrasonics to send and receive the data, we eliminate the need for expensive RF equipment such as high-speed oscilloscopes, spectrum analyzers, vector network analyzers, not to mention the cost of an SDR transceiver. All signals in this system can be viewed using an inexpensive oscilloscope—a basic 10 MHz bandwidth oscilloscope will work for viewing all signals. The ultrasonic system also eliminates the need to do any FPGA programming, which can be problematic for some embedded firmware engineers. Along with these advantages we also eliminate the pesky issues of RF systems (my apologies to RF engineers) such as parasitics, tricky board routing, antenna matching, not to mention working with s-parameters and smith charts.

SDR firmware development

The last reason for exploration of an SDR on this ultrasonic system is that the developed firmware is almost exactly the same as the firmware on a high frequency SDR RF transceiver. So, any knowledge gained will translate to these larger SDR systems. For some years I wrote firmware for an SDR system that transmitted and received in the 900 MHz ISM band. The system was designed to receive 64 channels at 100k baud simultaneously, while also transmitting 16 channels at 100k baud. Although it ran on a system with 3 ARM processors and 160 GFLOPs of DSP, the PHY level code is very much the same as the code used in this ultrasonic system. If you can develop firmware on this ultrasonic system, you can develop SDR firmware on larger, RF systems.

SDR systems

As you may know SDR is one of the hot areas in electrical engineering. It is currently being designed in and used in radar, military communications, cell phone services, satellites, and even car infotainment systems. From a high level view, it strips away much of the RF design in favor of digitized reception via analog-to-digital conversion, digital signal processing, and digital-to-analog conversion as close to the transmitting antenna as possible. There are a number of different architectures of SDR that vary in the amount of RF that is replaced by digital processing. Some of these architectures are superheterodyne, direct conversion, and direct sampling. The holy grail of SDR is direct sampling as shown in Figure 1.

Figure 1 Direct sampling architecture for an SDR where the RF is digitized very early on in the receive chain and no upconversion in the transmit chain, requiring the ADCs and DACs to have very high sampling rates.

The essence of this is that the received RF is almost immediately digitized going in—no LO mixing, no down conversion. Also, the transmit side does no upconversion in frequency. I say this is the holy grail because, in high frequency RF, this is very hard to do due to the speeds involved in the analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), and therefore the data rate that needs to be processed in the FPGA and/or the attached processor.

But direct sampling is doable when dealing with ultrasonic signals, in fact we’ll do this using a 16 MHz Arduino Nano and no FPGA.

The SDU-X system

Figure 2 shows the general design of a system I am calling the Software Designed Ultrasonic Transceiver or the SDU-X. The figure shows a minimum number of parts: a couple of ultrasonic transducers, a receiver amplifier/bandpass filter, an Arduino Nano, a DAC, and a transmit amplifier. Not shown is a handful of things like power supplies and some LEDs.

Figure 2 The SDU-X design with a minimum number of parts excluding power supplies and LEDs.

To assist in signal reception, there is a 3D printable parabolic dish for the receiver transducer. This dish mounts on a 3D printable tower that holds the transmitter transducer and has a set of sighting holes for aiming the dish. The dish gives about a 9 dB improvement in reception.

Current code allows for selection of different modulation types. Some are fully implemented for sending and receiving. Some are only implemented as transmitters and the receiver for that modulation type is something the user can experiment with creating. It is intended as something like a student exercise.

An SDU-X system, as seen in Figure 3, consists of two assemblies: one is called the Requester and the other the Responder. The hardware (PCBA) is the same for both, but they have different compiles of the firmware. When running, the Requester will send out a transmission asking for data from the Responder or for an action to be executed by the Responder. In the current firmware, the requested data can be something like the Responders SNR measurements, or a value from onboard analog input or digital I/O status. This data is transmitted back to the Requester who can then print it out on a serial port (available in the Arduino IDE). Requested actions can, for example, include having the Responder blink its LEDs or set some digital I/O high or low.

Figure 3 SDU-X system architecture with a Requester and Responder assembly connected to two 3D-printable parabolic dish antennas for the receiver transducer.

The SDU-X schematic

Let’s look at the schematic in Figure 4.

 Figure 4 Schematic of the SDU-X system with the receiver transducer (upper left) and transmitter transducer (upper right).

On the upper left side, we see the receiver transducer connected to an op-amp (U1A) acting as a band-pass filter with an adjustable gain of up to 100 (40 dB). Typically, this gain is just set to the maximum, unless the units are a few feet apart. The -3 dB roll-off frequencies are at about 7 kHz and 90 kHz. The transducer itself is also a good filter centered at 40 kHz. From my testing the 40 kHz acoustic band seems to be pretty quiet, so filtering of the input is not very critical.

Following the first stage is an op-amp (U1B) configured as a Sallen-Key band-pass filter with a gain around 10 (20 dB). When combined with the first stage, the receiver amplifier gives a total gain of 1000 (60 dB). The -3 dB roll-off frequencies of the combined two stages are at 38 kHz and 42 kHz, cleaning up the signal even further. The 0 to +5 V output of this amplifier is then run to an analog input pin of the Arduino Nano which is internally connected to the Nano’s 10-bit ADC.

Moving to the upper right of the schematic, we can see a pair of op-amps. The upper one (IC U6A) is configured as a non-inverting amplifier with a high-pass response rolling off -3 dB at around 1.5 kHz. The lower op-amp (IC U6B) circuit is configured as an inverting amplifier with about the same high pass response. When the outputs of these circuits are tied to the transmitter transducer they act as a differential driver, allowing the transducer to see a signal of approximately +/-12 V. Low-pass output filtering relies on the filter characteristics of the transmit transducer, which works very well. The transducer response rolls off -3dB at roughly +/-1 kHz. Note that the two resistors (R37 And R38) on the output the op-amps are to aid in stability, if needed, in driving the highly capacitive transducer. The input to these two op-amps comes from an 8-bit DAC (IC U3) which is driven directly from the Nano. Note that the two dual op-amps are TL082’s, selected primarily for the slew rate.

That’s essentially the complete direct sampling systems hardware.

A few more parts round out the schematic. The power supply generates 3 voltages; +8 VDC to power the Nano, +5 VDC to power the receiver op-amps and LEDs, and +15 VDC to power the transmit circuits op-amps. There are a couple of red/green bi-color LEDs on the board also, along with a few green LEDs for power indicators. The output power is a 10 to 13 VDC at 100 mA minimum. A simple AC adapter works fine.

There are spare digital I/O and analog inputs and a small proto area for experimentation and new features. Also, on the PCB are several strategic test points to monitor analog in and out as well as for use in monitoring streams of internal data such as received samples, correlation shape, flagging sync times, etc. Some test points are appropriately spaced and sized to allow for use with an oscilloscope probe ground spring for convenient hands-free probing.

Exploring software-defined radio (without the annoying RF) – Part 2

In the next installment, we will look at the firmware in the SDU- X. In the meantime, the PCB, schematic, 3D files for the parabolic towers, design notes, and firmware can be found at: https://www.thingiverse.com/thing:6268613

Damian Bonicatto is a consulting engineer with decades of experience in embedded hardware, firmware, and system design. He holds over 30 patents.

Phoenix Bonicatto is a freelance writer.

 Related Content

googletag.cmd.push(function() { googletag.display('div-gpt-ad-native'); }); -->

The post Exploring software-defined radio (without the annoying RF) – Part 1 appeared first on EDN.

Control chip preps for cryo temperatures in quantum computers

EDN Network - Thu, 12/07/2023 - 16:37

A consortium funded by Innovate UK and led by sureCore is implementing a cryogenic control chip on the GlobalFoundries 22FDX process, and Agile Analog is working closely with sureCore to implement and verify this cryogenic test ASIC for quantum computers. That will establish the viability of cryogenic ASICs aiming to migrate control electronics into the cryostat in order to be closer to qubits.

According to Paul Wells, sureCore’s CEO, quantum computing technology has been solidly proven by now in terms of the use of qubits. “There are various technologies to implement qubits, but they need to go to as low as 77 K (-196°C) down to the near absolute zero temperatures.” In other words, quantum computing outfits must prove that their qubits work fine at these temperatures.

See full article at Planet Analog, EDN’s sister publication

googletag.cmd.push(function() { googletag.display('div-gpt-ad-inread'); });
googletag.cmd.push(function() { googletag.display('div-gpt-ad-native'); }); -->

The post Control chip preps for cryo temperatures in quantum computers appeared first on EDN.

How do tire pressure sensors actually work?

ELE Times - Thu, 12/07/2023 - 13:51

Have you ever been driving down the road, noticed that annoying little warning light on your dashboard, and wondered how your car knows how much air is in each of your tires? Well, the answer is a tire pressure monitoring system (TPMS), which is an important safety feature in modern cars. In this blog post, we’ll take a closer look at how TPMS works and why it’s so important, so let’s dive in!

How does the technology behind tire pressure sensors work?

Each tire has a small sensor installed on the inside of the wheel rim. This sensor measures the air pressure inside the tire and sends a signal to the car’s computer system. The signal is transmitted wirelessly to a receiver located inside the car – this receiver can be located in various places, such as the dashboard or the tire pressure monitor module. Once the receiver receives the signal, it sends the data to the car’s computer system.

If the tire pressure drops below a certain level, the car’s computer system will trigger an alert, warning the driver of the low pressure in one or more tires. It’s important to note that TPMS doesn’t measure tire tread depth, alignment, or balance – it only measures air pressure.

Why are tire pressure sensors important?

Maintaining proper tire pressure is crucial for safe driving and fuel efficiency. Driving on underinflated tires can cause them to overheat, wear out faster, and even blow up, which can be dangerous. In addition, underinflated tires can reduce fuel efficiency by as much as 3%, which can add up over time.

TPMS helps drivers maintain proper tire pressure, which in turn helps ensure safe driving and optimal fuel efficiency. It’s also worth noting that TPMS has been mandated by law in Europe from 2014 onwards – meaning that all new cars must be equipped with a TPMS.

What sensor solution does Infineon offer for tire pressure?

Infineon offers a range of sensor solutions for tire pressure monitoring systems (TPMS), including the SP40 and SP49 pressure sensor, TLE4922 and TLE5501 magnetic sensors for detecting rotational speed and position, the XC27xxB family of microcontrollers with a built-in tire pressure sensor interface, and the AURIX TC3xx family of automotive microcontrollers with built-in support for tire pressure sensors. Infineon’s TPMS solutions are designed for high accuracy, reliability, and durability, meeting the demanding requirements of the automotive industry. With Infineon’s sensor solutions, car manufacturers can develop TPMS that enhance safety and lower fuel consumption, while improving the driving experience for car owners.

Figure 1. TMPS module

Additionally, Infineon has recently launched the new XENSIV SP49 tire pressure monitoring sensor. SP49 is a smart sensor that combines the MEMS technology with automotive knowledge to provide enhanced features for tire pressure monitoring systems. These features include the ability to sense the position and the load of the tire and detect if a tire has blown out.

LadurnerS_1-1696940513202Figure 2. XENSIV™ SP49

Tire pressure sensors are an important safety feature in modern cars, as they help drivers maintain proper tire pressure, which is crucial for safe driving. With Infineon’s sensor solutions, car manufacturers can develop TPMS that enhance safety and lower fuel consumption, while improving the driving experience for drivers.

 

The post How do tire pressure sensors actually work? appeared first on ELE Times.

BluGlass acquires contract manufacturer GaNWorks Foundry for US$800,000

Semiconductor today - Thu, 12/07/2023 - 13:45
BluGlass Ltd of Silverwater, Australia — which develops and manufactures gallium nitride (GaN) blue laser diodes based on its proprietary low-temperature, low-hydrogen remote-plasma chemical vapor deposition (RPCVD) technology — has reached an agreement to acquire its Silicon Valley-based commercial contract manufacturing partner GaNWorks Foundry Inc for US$800,000, comprising 85% in 17,436,556 new BLG ordinary shares (US$680,000) issued from within the firm’s‘s existing capacity under ASX listing rule 7.1, plus 15% in cash (US$120,000, funded by the receipt of the firm’s $7.3m R&D rebate)...

STMicroelectronics Accelerates Edge AI Adoption to Help Companies Transform their Products

ELE Times - Thu, 12/07/2023 - 12:42
  • Announcing the ST Edge AI Suite, a comprehensive, integrated set of software and tools offering a simpler, more cost-effective way for developers and companies to embed AI-enabled ST products into industrial, automotive/mobility, consumer, and communication applications
  • ST offering developers and companies a comprehensive ecosystem with a broad range of hardware with free software and tools, supported by partnerships with cloud services and AI toolchain providers
  • Companies of any size to benefit from unconstrained edge AI deployment, accelerating its adoption globally

STMicroelectronics, a global semiconductor leader serving customers across the spectrum of electronics applications, is introducing its comprehensive ecosystem offer for companies to transform their products with edge AI. The announcement of the ST Edge AI Suite, an integrated set of software tools free-to-use with ST hardware, takes the offer to customers one step further, enabling them to jumpstart the design and deployment of billions of connected, autonomous things embedding artificial intelligence locally. The ST Edge AI Suite will simplify the development of customers’ AI solutions by exploiting ST’s broad range of hardware (general-purpose and automotive microcontrollers and microprocessors, smart sensors) and related tools for embedded AI optimizations. It will expand and integrate the multiple software tools, evaluation, and development kits introduced over the past 10 years, while leveraging the existing AI ecosystem of machine learning (ML) frameworks and key partners (such as Nvidia and AWS).

“We are moving towards a world with tens of billions of connected, autonomous things bringing value and convenience to their users throughout all aspects of consumer life and enterprise productivity. To achieve this, AI algorithms will need to be run both in the cloud and on-device, at the edge across a broad range of devices: smartphones and connected personal devices, smart home and building control systems, industrial machines, cars and many more,” said Jean-Marc Chery, President and CEO of STMicroelectronics. “ST products are already at the core of all those devices, but it is their combination with the industry-leading, free software suite we are announcing today that will make the difference. This combination will enable the transition to a more intelligent edge, empowering customers of any size to deploy edge AI more easily and build their vision of this connected future leveraging ST’s hardware portfolio.”

A preview of the ST Edge AI suite capabilities was presented today at ST’s online Edge AI Summit. ST’s industry-leading offer will empower embedded developers who want to create optimized machine learning models, data scientists who want to run their model on an embedded device, and product designers and creators who want to redefine product greatness.

With free access, ST will enable customers large and small, pooling resources and knowledge into a community-driven approach. The Suite will further enable this transformation by federating the tools and their users around a broader edge AI community.

The first release of the ST Edge AI suite will be available in the first half of 2024.

More information about the benefits of the adoption of edge AI
Edge AI is a crucial technology for businesses to transform their products in today’s connected world by bringing intelligence and decision-making capabilities closer to the data source. This offers numerous benefits in terms of speed, power consumption, privacy, security, and cost-efficiency empowering businesses to create more intelligent, responsive and efficient products that meet the demands of today’s fast-paced and data-driven world.

Examples of businesses transforming their products with ST:

15-40% performance improvement for washing machines:
A major home appliance maker is currently adopting this solution and we should see their product on the market next year. The first machine learning algorithm is creating a virtual sensor approach, estimating the weight of the clothes to be washed based on the motor current measurement. Another machine learning algorithm is collecting data from a 6-axis motion sensor to enable drum collision avoidance by calculating if the drum will touch the outer shell of the washing machine. Based on the algorithm input, a program drives the motor using exactly the current needed and adjusts the water and detergent used to save between 15 and 40% energy and water for a washing cycle. Both algorithms have been developed with NanoEdge AI and are running on an STM32G0 MCU together with an ST 6-axis motion sensor.

Ultra low power PC activity monitoring:
The HP engineering team worked closely with ST to develop and train AI models that recognize different user activities based on device and user motion. Multiple use cases were addressed, including scenarios where the laptop is placed on a table, on the user’s lap, or carried inside a bag and taken out. This helped create an AI model specific to HP devices that was put on a smart 6-axis motion sensor. But the really interesting part here is the power consumption. This sensor is running an edge AI algorithm at 34 microamps. This allows HP computers to detect changes and respond accordingly. And most importantly, conserve the battery power for more critical tasks.

EV motor operation and maintenance optimization:

ST is working with the HPE Group to optimize the operation and maintenance of motors in electric vehicles. The challenge here was to monitor the internal temperature of the rotor of an electric motor in actual use, so that power output could be optimized for more efficient and safer operation.  This is something that could be done in a lab with the rotor exposed but is not possible in an actual running motor in a vehicle. The solution was to train a model and build a virtual temperature sensor with edge AI to estimate the internal rotor temperature from the external temperature measurement. The algorithm runs on the microcontroller that controls the motor – a Stellar automotive MCU. The same MCU also runs an AI algorithm that detects potential anomalies through the analysis of vibrations. The same approach can be used with other critical components, like EV batteries, where the internal temperature of the battery is not practical to measure but an edge AI model can simulate it from an external measurement.

 Additional technical information
ST’s strategy on AI relies on the availability of a comprehensive, integrated set of tools (some of them already available today), technical and educational examples, and an innovative, unified optimizer of embedded AI solutions called ST Edge AI Core Technology. The ST Edge AI Suite addresses the needs and requirements of different profiles, like Data scientists, embedded SW developers and HW System Engineers.  The suite is easy to use, with a simple and intuitive interface, available in different fashions (Desktop, CLI, Web, API).

  • ST Edge AI Suite works across multiple ST hardware platforms: will be working across: STM32 general-purpose MCUs, including the already announced portfolio with AI hardware acceleration, STM32N6 and STM32 MPUs built for industrial applications; Stellar automotive microcontrollers, supporting carmakers in their transition to software-defined vehicles with predictive maintenance of the electric powertrain to extend vehicle lifetime or battery management systems to maximize energy efficiency embedded intelligent sensors (based on intelligent sensor processing units, or ISPU, machine learning cores, or MLC, as well as leveraging Time-of-Flight sensors for advanced imaging), ideal for applications in industrial, automotive and anything from wearable accessories to high-end personal electronics applications. All are supported by a broad range of evaluation and development boards.
  •  A critical component of the ST Edge AI suite is ST Edge AI Core which brings together all the SW and tools engineers need at each step of their project: The ST Edge AI Core can import ML and NN algorithms from the most widely used ML frameworks, provide a detailed analysis, optimize the algorithm for the selected devices (sensors, MCU, MPU), validate against the original model, and finally map the resulting embedded AI solution on the selected device. It will be possible to benchmark the same AI algorithm on different platforms, in pure SW or exploiting specific HW accelerators, to assess accuracy and inference time in a few clicks.
  • NanoEdge AI Studio autoML tool becomes free for STM32, and is now available for all ARM Cortex-M based MCUs: In parallel, ST’s autoML tool NanoEdge AI Studio is getting an upgrade to the benefit of customers globally: its use will become free. The deployment of libraries created by NanoEdge AI Studio will now be at no cost for unlimited deployment on any STM32 microcontroller. In addition, as NanoEdge AI Studio targets all ARM Cortex-M based microcontrollers, clients will now be able to build and deploy libraries, including unique on-device learning, on other ARM Cortex-M microcontrollers under a special license agreement.

The post STMicroelectronics Accelerates Edge AI Adoption to Help Companies Transform their Products appeared first on ELE Times.

CSconnected welcomes UK’s £160m semiconductor investment zone in South Wales

Semiconductor today - Thu, 12/07/2023 - 12:22
A recently announced investment zone in the UK is to focus on further strengthening the CSconnected semiconductor cluster based in and around South Wales...

ROHM expands library of LTspice models to over 3500 by adding SiC and IGBTs

Semiconductor today - Thu, 12/07/2023 - 12:12
Japan’s ROHM has expanded its lineup of SPICE models for the LTspice circuit simulator, increasing its number of LTspice models to more than 3500 for discretes...

Navitas ranked top 50 in Forbes’ 2024 Most Successful Small Companies

Semiconductor today - Thu, 12/07/2023 - 12:06
Gallium nitride (GaN) power IC and silicon carbide (SiC) technology firm Navitas Semiconductor Corp of Torrance, CA, USA has secured 49th position on Forbes’ 2024 America’s Successful Small Companies list. The ranking is recognition of the firm’s growth based on strong demand for its high-efficiency, wide-bandgap (WBG) GaN and SiC power components, across growing and diverse global markets and an expanding customer base...

Pages

Subscribe to Кафедра Електронної Інженерії aggregator - Новини світу мікро- та наноелектроніки