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Lattice Blog

Watch the Lattice sensAI Solutions Stack Deliver Low Power Smart Vision to the Edge

Watch the Lattice sensAI Solutions Stack Deliver Low Power Smart Vision to the Edge

Posted 07/10/2019 by Hussein Osman

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

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sensAI 2.0 Blog

Lattice sensAI Delivers 10x Performance Boost for AI on Edge Devices

Posted 05/20/2019 by Hussein Osman

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?

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Architecting Low Power AI

System Architecture Options for On-Device AI

Posted 11/14/2018 by Deepak Boppana

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.

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Home is where AI is

Home is where AI is

Posted 09/04/2018 by Hussein Osman

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...

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Mobile-Influenced FPGAs Going Broad

Mobile-Influenced FPGAs Going Broad

Posted 04/17/2018 by Hussein Osman

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.

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Implementation of Artificial Neural Networks at the Edge

Implementation of Artificial Neural Networks at the Edge

Posted 07/05/2017 by Hussein Osman

Imagine private security systems that can differentiate between an intruder and your neighbor’s dog, smart TVs that can scan the room and automatically turn off when no one is present, and cameras that can perform forensic analysis and identify suspicious behavior before a crime occurs. The applications for deploying artificial neural networks at the edge are endless. Coming up with ideas is easy, but getting to the implementation is not that simple.

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Distributed-Heterogeneous-Processing

Distributed Heterogeneous Processing Opens New Applications for FPGAs

Posted 12/20/2016 by Abdullah Raouf

Increasingly lower cost sensors, the migration to higher performance I/O interfaces, and the demand for “always-on, always-aware” functionality all present new challenges for designers of battery-powered mobile devices. Engineers building everything from phones and drones to wearables and industrial equipment are facing the same problem. 

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