Lattice Blog

If you missed the 2019 Embedded Vision Summit, check out the latest sensAI demos from Lattice to and see what smart vision can enable in Edge devices

Read the latest automotive and industrial news from Lattice: New MachXO3D FPGA brings secures entire systems with hardware root-of-trust, Crosslink bridge connects industrial displays to mobile processors, new 3D Depth Mapping demo, new Scaler IP core for ECP5, and a new version of Diamond Software v 3.11 adds support for MachXO3D.

We live in an increasingly connected world, filled with communication systems, cloud computing and Edge devices working together to increase safety, comfort and convenience. But with that connectivity comes risk. We’re all familiar with how hackers exploit vulnerabilities in software to illegitimately access systems, but hardware is also vulnerable.

A year ago we launched the Lattice sensAI solutions stack. Since then, the need for AI at the Edge has continued to grow. Consider this statistic from Tractica: by 2025 the market for Edge-based AI chipsets is forecasted to hit $51.6 billion (that’s over three times their forecasted revenues for cloud-based AI chips). Why all the interest in chips that support AI at the Edge?

We’ve published a couple blog posts exploring how support for AI-powered imaging systems in embedded devices operating at the network edge can benefit specific applications in smart factories and smart homes. But embedded vision can also benefit the retail customer experience.

Last week at Embedded World, the Nürnberg Exhibition Center was abuzz with conversations about today’s hottest tech topic, artificial intelligence (AI), as embedded engineers prowled the show floor looking for ways to quickly and easily add AI support to future products. Lucky for them, Lattice Semiconductor had the solutions they were looking for.

With the evolution of SMART cities, homes and factories, there is a multitude of situations that demand accurate proof of the presence of a person…and perhaps his or her positioning and actions. In a modern factory using Industrial IoT (IIoT)/ Industry 4.0 practices, safety must not be left behind.

How often is low power the determining factor for success? Certainly when designing solutions for AI inferencing in always-on edge devices, the power consumption must be measurable in milliwatts. Think about it: AI at the edge solves real world problems, and is – or very soon will be – everywhere.

There is an emerging, though not widely known attack vector for hacking a server: firmware. Last month, researchers at ESET published a report on Lojax, a rootkit (firmware hacking tool) believed to have been developed by Sednit, the notorious cyberespionage group linked to Russian military intelligence.

A new generation of AI-based edge computing applications demand a wide range of performance requirements. But how can developers build edge solutions that consume little power and occupy minimal footprint at low cost without compromising performance?

It may seem obvious but safer factories are not only a better work environment, they are also more productive. And some of the machine vision techniques that are now being employed to ensure worker safety are also applicable to aid production processes.

Very soon most homes will have Siri, Alexa, Google Home or similar. Many already have all three. The acceptance of such sophisticated AI systems as an everyday, normal addition to the living room says much about the human condition to imagine, conceptualize, innovate, experiment with...

Over recent decades system design has evolved from one processing topology to another, from centralized to distributed architectures and back again in a constant search for the ideal solution.

New Technology Promises to Accelerate Deployment of Machine Learning Inferencing Across Mass Market, Low-power IoT Applications

On the path for faster time-to market and reduced development time and cost, rapid prototyping is required for many electronic designs. To accomplish this task, designers need to reuse existing hardware and software building blocks. The emergence of vision technologies further complicates this path as many design engineers have experience in everything but embedded vision!

There is an old saying that two heads are better than one, and nothing could be truer when it comes to strategic partnerships. In today’s business environment, partnerships are of the greatest value to organizations that are focused on supporting the end-customers.

The complexity of implementing intelligent Edge solutions is causing many issues for designers, including accommodating devices with new and legacy interfaces in a variety of applications. Some of these applications also require compute engines capable of processing data collected at low power and cost.

You may be asking yourself, “What does a nineteenth century architect have to do with FPGAs?” A key decision facing many system architects is which FPGA families to use in their next project. Lattice currently offers four classes of FPGAs: iCE, ECP, Mach and CrossLink. It is often tempting to think of FPGAs as just a blank canvas, ready to be filled with digital logic.

Virtual reality (VR) is more than just captivating displays with fancy 3D graphics. Fluid feedback of users’ gestures, postures, movements and position is pivotal for a complete immersive experience. If the user’s actions cannot be quickly and accurately tracked, it would be impossible to realize the fighting scene...

We recently introduced our next generation FPGA design software: Lattice Radiant software. Embracing our corporate mission of enabling edge connectivity and computing designs, the Radiant software was specifically created for the development of edge applications. It is a full-featured FPGA design tool suite and yet simple to use.