Lattice Blog

AI is evolving at a rapid pace not just from continued technology advancements, but also by various industries’ demands and requirements. With the surge of Large Language Models (LLMs) and Generative AI, the industry is grappling with the intense compute capabilities these cloud-based AI applications require to process big data, as well as train and deploy advanced AI models. Now, the industry is seeing AI being implemented into client devices including PCs and smartphones, and Edge applic...

In today’s data-driven world, artificial intelligence (AI), machine learning (ML), and digital twin technologies are transforming industries, revolutionizing processes, and reshaping enterprise business environments. With more than 328 million terabytes of data created each day, it has become the new “oil” – providing the energy needed to fuel next generation digital systems. However, the effectiveness and reliability of these technologies heavily depends on the quality ...

Every year, PC manufacturers introduce an array of new laptops packed with the latest technologies and features, all designed to deliver great user experiences. Historically, these came in the form of form factor innovations, screen enhancements, and user interface improvements. However, the growing popularity of AI (artificial intelligence) and ML (machine learning) has opened a new world of possibilities and PC manufacturers and ecosystem giants are all looking for ways to add these advance ne...

On-device AI inference capability is expected to reach 60% of all devices by 2024, according to ABI Research. This underscores the rapid speed of AI innovation to take place in the last few years that has required engineers to come up with flexible design models as they transition from the cloud to the edge. The trend is driven by advances in ultra-low latency, security, bandwidth limitations, and privacy. Lattice FPGAs and software solutions help enable acceleration of future models with exist...

The global smart toy market is projected to grow to nearly $70 billion by 2026, per Transparency Market Research. Smart toys take a variety of shapes, like electronic pets, robots, smart train kits, and more. When you hear the word toy, you likely automatically think these are for kids, however some are developed specifically targeting older generations. The current senior toy marketplace contains products that range in utility from providing companionship to increasing cognitive skills to facil...

Lattice CertusPro-NX FPGAs outperform similar devices in data processing performance, system bandwidth, memory density, and support for small form factors.

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.

If you’re looking at ways to implement Edge AI, be sure 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.

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