Lattice Blog

One of the truisms of modern business is that as industries mature, they tend to specialize. Customer demands typically become more sophisticated over time and that leads to products optimized to meet those specific needs.

As the leader in low power programmability, Lattice has set the standard for what low power, small form factor FPGAs can do in terms of performance and power consumption. We earned and maintained that position through a culture committed to customer support and consultation. For example, before we began active development of the Lattice Nexus platform, we met over 100 different customer design teams to get their input on the

There is growing interest among embedded vision developers in adding AI/ML technology to their designs to give them the intelligence needed to run applications like object counting or presence detection. However, supporting AI/ML in embedded vision applications can be challenging, particularly as embedded vision applications trend towards increasing complexity.

At Linley Fall Processor Conference, Lattice shared latest updates to Lattice sensAI™, a solution stack for next-generation smart devices with extremely low power consumption.

Read the latest consumer news from Lattice: Enhanced sensAI Stack Enables More Accurate, Lower Power Edge Solutions, Free Webinar Explores Low Power AI Design, CrossLink FPGAs Simplify Use of Embedded Vision

Lattice Semiconductor sponsored the Microsoft Sensors Hack, part of the larger 2019 Hackathon event, where Microsoft teams used the Lattice sensAI solutions stack for smart vision systems to create potential use cases for AI and sensors.

如果你观察的方法来实现边缘AIsure to read about the use cases for the Lattice sensAI solution stack for always-on Edge devices covered at the end of our white paper.

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.

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.