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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?