莱迪思博客

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...

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 ...

Interest in edge computing has surged as organizations across industries seek smarter ways to automate processes, enhance productivity, and optimize labor. By processing data closer to its source, edge systems can provide benefits like reduced transmission and storage costs and strengthened security. They can also enable the development of advanced machines and devices, from autonomous mobile robots (AMRs) and humanoids to smart medical devices, which can operate with precision and speed. Thes...

Across industries and use cases, computing capacity is shifting away from centralized servers and towards the edge. Whether in the form of autonomous vehicles, smart sensors, or other technological solutions, today's intelligent applications demand faster decision-making and increased autonomy. This shift is especially prevalent in the Industrial, defense, and aerospace industries. The unmanned aerial vehicles (UAVs) and drones used in defense applications rely heavily on edge intelligence to...

Planning and execution are two very different beasts. A project may appear straightforward on paper, staying within budget, on schedule, and technically sound, only to hit roadblocks in the real world. However, turning ideas into reality isn’t always smooth, and success depends on how well we anticipate and navigate the unknowns. This gap between concept and execution is especially pronounced in the fast-growing realm of edge artificial intelligence (AI). In a recent roundtable discussion...

Everyone, it seems, is now talking about how they’re planning to integrate AI into their devices, their factories, their workflows and, well, everything. But how they actually plan to make that happen isn’t always clear. Part of the challenge, of course, is that different workloads and different environments require different types of solutions. For those looking to integrate AI-powered capabilities into edge computing-based offerings, there are a relatively broad range of ways to ac...

Human-machine interfaces (HMIs) are rapidly evolving, driven by trends such as Automotive personalization, sustainable always-on interfaces, hygienic touchless user interfaces (UI), consistent user experience (UX) across platforms, voice activation, and Industrial automation for labor and safety needs. Regardless of their specific drivers and/or use cases, modern HMIs must be smarter and more dynamic – shifting from command-based to context-aware systems that bridge the human-machine...

Robots have rapidly evolved from science fiction into a cornerstone of modern industry. Today’s autonomous systems can execute complex tasks with minimal human oversight – transforming how we work, live, and move. But achieving this level of intelligence and reliability in real-world environments requires more than just advanced software. It demands robust hardware, deterministic processing, and scalable system architectures that can support safe, real-time decision-making under dema...

网络边缘AI正在改变机器与世界的交互方式，它可以直接在数据源附近实现智能，带来实时、情境感知的决策。在汽车和工业环境中，这一转变推动了更智能的传感器、自动化和更先进的人机交互界面（HMI）。但在边缘部署AI面临着计算能力有限、严格的功耗预算和紧凑的硬件尺寸等挑战。 在我们最近的LinkedIn Live小组讨论中，莱迪思的专家们探讨了工程师如何利用莱迪思的FPGA以及Lattice sensAI™解决方案集合，实现具备高性能、安全性和灵活性的智能、实时嵌入式体验。 边缘AI为何势头迅猛 AI不再局限于云端。通过将智能嵌入到边缘设备，工程师可以降低延迟、增强隐私，并避免带宽瓶颈，这对现实应用中的安全和性能至关重要。 边缘AI广泛应用的主要驱动力包括： 爆炸式增长的传感器数据需要本地处理 嵌入式和移动系统的能耗限制 安全关键环境对实时响应性的需求 依赖云端带来的隐私与安全问题 降低部署成本的压力 边缘AI让道路和工厂上的机器变成能实时学习与响应的自适应系统。 FPGA在边缘AI中的角色 工程师不必为边缘场景重做AI模型&mdash...