Embedded Computing Design

Attend this three-hour training session, and literally walk away with the latest hardware and software to serve as the foundation of your Edge AI application. Work with AI-native Edge-AI compute solutions and be among the first developers to use the Synaptics Reference Developer Kit, which offers you a chance to get ahead of the curve in a classroom environment. You’ll be trained on the tools that enable real-world AI capabilities to be deployed on your Edge AI application.

The needs of embedded computing applications are constantly evolving, requiring versatile feature sets, rapid processing needs, reliable ruggedness, and more. The ability to maintain flexibility and programmability in one solution enables applications like automotive, industrial automation, AI & ML, IoT, and image and video processing to efficiently progress along with the ever-changing high-performance needs.

The push to bring processing closer to the edge is undeniable, and the demand for embedded systems to provide that processing with AI, ML, and IoT capabilities is only going to continue to grow in every vertical.

Control system architects and embedded software developers are often tasked with initiating and advancing systems, each with diverse requirements in demanding environments. To streamline the development process, developers can make use of an out-of-the-box solution that provides simplified workflows, easy integration and functionality, hardware inclusion, accessibility, and more.

On this episode of Embedded Insiders, Tomide Adesanmi, Co-Founder and CEO of Circuit Mind, dives into his company’s deterministic AI platform that’s designed to help circuit designers quickly take their design process from architecture into schematics. 

Check out Circuit Mind’s public launch webinar here: https://www.bigmarker.com/eetech-media/Circuit-Mind-Live-Launch-The-World-s-First-Deterministic-AI-for-Professional-Electronic-Engineers?utm_bmcr_source=cm-other

Welcome to the third installment of Scoping Out the Software Defined Vehicle, a series brought to you by the Eclipse Foundation to take an inside look at one of the top trends in the embedded space — software-defined vehicles.

Versatility among embedded applications is key. Especially today with the integration of critical and advanced technologies like artificial intelligence (AI) & machine learning and edge computing, and capabilities like fast processing, reliable video graphics, power efficiency, and more.

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As we stand on the brink of 2024, the landscape of our industry is on the verge of unprecedented transformation. This year holds special significance for Digital Core Design (DCD) as we celebrate our 25th anniversary – a quarter-century of actively shaping the IT market since our inception in 1999.

You know how to code your solutions and, sure, you can create brilliant applications. No question, but how do you get it to market, and how quickly. As everyone knows time to market can make or break a product, and sometimes getting there too slow is worse than not getting there at all.

Industrial environments today are retrofitted with high-processing, low-latency, and real-time networking solutions that enable them to handle complex workloads and data analytics with reliability and efficiency.  

Memory technologies are often integrated into demanding applications, industrial being one of most critical and expansive, requiring high reliability, versatility, low power consumption, and much more. There are various types of memory that are equipped with these features; however, some are better suited as they’re capable of enabling higher densities at a lower cost.

On this episode of Embedded Insiders, Meghali Chopra, CEO at SandBox Semiconductor, gives insight into her company’s AI-enabled modeling platform that accelerates the development of semiconductor manufacturing processes. 

Today’s modern computing environments require scalable processors that are capable of meeting the growing demand for raw computing power, flexible architecture, and accelerated performance workloads for artificial intelligence, data analytics, networking, storage, and high-performance computing.

Networking applications are an integral part of our daily lives, supporting efficient communication, enabling entertainment and collaboration, and providing access to information. For today’s computing networks and internet, developers creating these applications can benefit from standardized interfaces that allow network code to be written seamlessly across various systems, like real-time operating systems.

Welcome to the first installment of Scoping Out the Software Defined Vehicle, a series brought to you by the Eclipse Foundation to take an inside look at one of the top trends in the embedded space — software-defined vehicles. In this installment, we spoke with Martin Schleicher, head of software strategy at Continental, about building an open platform for use in SDVs.

Aurora Labs’ OTA Auto Update uses AI to identify changes in vehicle source code and integrate into the software developer’s toolchain to enable six times smaller update files than current OTA solutions.

Using machine vision, an embedded computer can interpret and understand visual information from various inputs, including cameras and other sensors. In an industrial setting, this information is combined with image-processing algorithms to help make informed and extremely valuable decisions.

Versatile connectivity, high-performance computing, and scalable single board computing solutions have proven necessary in today’s critical, real-time embedded systems, IoT, networking, and telecommunications applications that aid in efficient, streamlined data processing.

On this episode of Embedded Insiders, Artem Aginisky, product line manager of arm-based processors at Texas Instruments, dives into the topic of edge AI, specifically, how he defines edge AI and how TI is working on democratizing edge AI through innovations in microprocessors and software.