Today's autonomous robots are being tasked with increasingly important and dynamic roles across industries. From the autonomous mobile robots (AMRs) that move goods around warehouses to the drones that handle last-mile delivery, these devices are trusted to operate at the edge with little direct human oversight or handling. These robots rely heavily on the sensors that enable them to perceive, understand, and interact with the world around them. As edge AI models are brought in to support more ...
Today’s industrial robots are becoming more capable, moving from simple fixed-function systems to perception-driven platforms. Advancements in sensor and processing technology are enabling these systems to better perceive their surroundings, adapt to changing conditions, and make more reliable real-time decisions, if developers can overcome the new processing, power, and latency challenges that come with added complexity. In our latest LinkedIn Live panel discussion, experts from Lattice,...
Drivers expect their vehicles to be safe whether they are behind the wheel or not. But delivering on that expectation, with a system that monitors, detects, and responds around the clock, has proven harder than it sounds. Last year saw car thefts in the US drop to their lowest level in decades, down 23% year over year. Still, a theft occurs every 48 seconds, causing staggering losses for individuals and organizations. Always-on monitoring has emerged as a potential solution, enabling smarter al...
As demand for artificial intelligence at the edge continues to grow, it has become increasingly difficult for designers and developers to support. Constrained edge systems often lack the power, processing, and space required to run these high-performance workloads effectively. In a recent webinar hosted by Embedded Computing Design, experts from the Lattice team discussed the growing role that flexible field-programmable gate arrays (FPGAs) play in the development and deployment of edge AI solu...
As AI adoption accelerates, workloads are no longer confined to centralized datacenters. Instead, AI is scaling across cloud infrastructure, edge systems, industrial platforms, robotics, and physical AI devices. This shift is fundamentally changing how systems are designed. While CPUs, GPUs, and other accelerators continue to anchor AI performance, modern architectures are becoming more modular, more distributed, and far more dependent on the silicon that surrounds those primary compute engines....
Supporting today’s growing landscape of distributed, autonomous devices is no simple feat. Whether it is industrial robots, autonomous drones, or in-vehicle safety systems, each of these increasingly intelligent solutions requires real-time processing capabilities to function. Supporting these capabilities requires moving artificial intelligence (AI) and machine learning (ML) applications away from centralized cloud services and closer to the cameras, radar systems, and other sensors that...
Present-day edge AI systems rely heavily on multi-modal sensor fusion, such as camera, lidar, and radar, to enable accurate, real-time decision-making. Existing platforms, such as NVIDIA® Jetson Orin NX, are equipped to adequately support multi-camera use cases. To advance this further, NVIDIA’s latest Jetson Thor series modules have been combined with the Holoscan Sensor Bridge board – running on a Lattice FPGA – to address the increase in sensor counts and synchronization...
The rapid expansion of connected, intelligent machinery is transforming Industrial infrastructure as we know it. As devices at the edge take on more responsibility, engineers and developers face rising pressure to enable connectivity while maintaining overall system effectiveness and security. Industrial organizations must keep pace with digital transformation and stay resilient against expanding and complex cyber threats. To strike this balance, Industrial infrastructure must become more cyber...
When it comes to making predictions, sometimes you really have to go out on a limb and sometimes, well, it’s pretty easy. In 2026, there’s zero doubt that AI, generative AI, and agentic AI will continue to be the key buzzwords driving the tech industry forward. Perhaps a bit less obvious is this year should also mark the beginnings of a growing opportunity in Edge AI, where AI-focused workloads can run in disconnected environments and/or locally on client type devices. The combinati...
Artificial intelligence (AI) is rapidly moving out of the datacenter and into the real world, powering everything from industrial robots to autonomous vehicles and smart infrastructure. Edge devices have become the new frontier for AI, driving smarter factories, safer vehicles, and more responsive cities. Meeting the needs of edge applications involves using AI that is efficient, adaptable, and scalable, since these scenarios often involve unique technical constraints and possibilities. As AI ...