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...

The importance of AI and ML, the rise of edge computing, and the need for flexible programming in AI. Artificial Intelligence (AI) is one of the biggest buzzwords in the technology industry today and is often thought of in broad terms to describe connected “smart” technology. However, when you think about all the different features enabled by AI and the outcomes AI solutions are designed to deliver, it becomes clear that the AI landscape and AI development is incredibly complex. The ...

The AI/ML revolution continues to gain traction across multiple applications, particularly Edge applications. Edge devices like security cameras, robots, industrial equipment, Client PCs, and even toys can now support AI/ML capabilities that provide users with new capabilities and experiences. According to industry analyst firm ABI Research, the Edge AI chipset market “has experienced strong growth in the past and is expected to continue to grow to US$71 billion by 2024, with a CAGR of 31%...

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

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.