Consumer

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.

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?

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!

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.