Endless patching is a race that cannot be won. To build sustainable, secure IoT solutions we must change that ineffective paradigm. To appreciate what we can do differently, we should start by considering both the defender and attacker perspectives. This session will provide a unique view of that...

A forecast by International Data Corporation (IDC) estimates that there will be 41.6 billion IoT devices in 2025, capable of generating 79.4 zettabytes (ZB) of data. That is a lot of data floating around cyberspace. As the number and types of devices increase, new security challenges are rapidly...

When it comes to balancing the security of your IoT devices with the need to enable your DevOps team to manage and maintain your fleet, have you ever thought about how great it would be to be able to connect to an already deployed device in the field and securely run a remote debugging session on it...

Traditional testing of IoT device software can leave subtle bugs undetected, leading to vulnerabilities. Today's formal methods mathematically guarantee code security and quality, and the absence of bugs. Over the last decade, mathematical methods have been democratized to go beyond formal ver...

As the United States emphasis on microelectronics increases, the focus on leveraging advanced technology from trusted and secure semiconductor manufacturers is critical. The strategic collaboration between Micron and Mercury brings together key industry leaders through true customer focus, technolog...

Making IoT products is hard — especially if you want to build a great, profitable product on a tight schedule with limited resources and technical knowledge. Find out how to get around these challenges using one simple, cost-effective solution: Infineon’s AIROC™ Cloud Connectivity Manager, which sup...

Globally, aviation accounts for 4.9% of the CO2 and non-CO2 emissions. Industry is taking steps to reduce aircraft emissions and improve fuel efficiency in an effort to combat climate change but with twice as many air travelers expected by 2037, those efforts alone will not be enough. It’s clear...

In this instructional video series, we’ll offer insight and instruction on devices from the Raspberry Pi foundation. We’ll first focus on the powerful (and available) RP2040 microcontroller and its associated boards, introducing it and delving into how to program it. Further classes may even deal wi...

In the first class of this on-going series of “Developing with Pi,” we will introduce the RP2040 and walk through its feature set and capabilities, proving the engineering community with the foundation it’ll need for subsequent classes. In addition, we’ll present several dev boards that take advanta...

A variety of medical device modalities and industrial edge applications can be improved with the addition of new features enabled by AI. Developers of these devices and equipment need a platform that enables AI at the edge. The NVIDIA IGX hardware and software-based platform with long term suppor...

Machine learning inference has thoroughly penetrated embedded applications, especially with visual and spatial data like images and radar/lidar. Texas Instruments has a scalable processor portfolio for vision inference at the edge. From inexpensive Arm®-only solutions to computationally-efficient ha...

The percentage of systems that incorporate AI continues to grow. Hence, it’s now clearly on the radar for most developers/engineers, in applications ranging from consumer to industrial to medical healthcare. With the compute performance available today, the technology has migrated to the Edge of...

In this session we will work with ModusToolbox and our ModusToolbox & Friends partners and how we can combine multiple capabilities from connectivity, audio and machine learning through the example of Audio classification. You will learn how ModusToolbox is extending to external tools that make it e...

Did you know more than 1 billion tons of food is wasted every year? What if we could reduce that? What if optimized AI was the key? Building AI applications to solve real-world problems is our common challenge as developers. To solve a challenge like food waste, optimization is important. In t...

The buzzword soup of Machine Learning, AI, edge computing, and IoT can be overwhelming (and intimidating) for many developers. In this talk, we will start from a beginner's view of these concepts and evolve to see how we can merge the worlds of ML with the Internet of Things to create a few silly, i...

While many (most?) of the uses of AI in automotive are for autonomous-drive-related features, there is a growing need for AI to be used in software development and testing. Analysis of the relationships and dependencies between software functions, and of their behavior in run-time will generate acti...

Algorithms for processing graphical objects such as raster images and 3D point clouds can quickly become very compute-intensive. Many of these algorithms are excellent choices for parallel processing optimizations. Developing custom general-purpose GPU code in C++ is easier than you might think, and...

Today, the state-of-the-art solution to enable high performance imaging radar is to increase the antenna count and processing capabilities of the system. Fundamentally, this approach is not an optimal solution for automotive and robotics applications as the imaging radar available on the market use...

Deep learning models are advancing at a fast pace, creating difficult dilemmas for system developers. When you begin developing an edge AI system, you select the best available model for your needs. But by the time you’re ready to deploy your product, your original model is obsolete. You’d like to u...

When it comes to AI, there are inherent dangers one must consider at the board/system level, or when moving over to the edge. Malicious attacks are growing more sophisticated, and hackers are finding new ways to cause disruption to critical systems. With sensor data vulnerable to modification, the v...

AI technology is the focus of many organizations. This discussion will explore the common themes around adoption to understand what AI technology is being used and why.

AI at the Edge is no longer “something down the road” or even “bleeding-edge technology.” Some consider it to be mainstream technology. But that doesn’t make it any less complex. In this session, we will look at the hardware and software components that are required to implement AI at the Edge, and...

Many AI applications are based on vision, and most of those applications contain multiple imaging sensors. The pattern/image-recognition software has improved to the point where accuracy is in the high 90%s. But the real trick is making sure that the software is tuned for your application. In this s...

In the recent past, the Edge computer would collect and process data from the sensors/endpoints, then either take an action directly or send a signal back to the endpoint for the action to occur. Today, the intelligence that’s built into the endpoints is more than adequate to process the data and ta...