莱迪思博客

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...

In early 2026, a quiet shift became impossible to ignore: artificial intelligence (AI) moved from helping defenders to operating like an attacker at scale. The cybersecurity community took notice when researchers revealed that an advanced AI system, known publicly as Claude Mythos Preview, was able to independently discover and exploit serious software vulnerabilities. Many of these weaknesses had existed for years in widely used operating systems and software, despite extensive testing and revi...

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...

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...

The continued evolution of AI is reshaping the foundation of datacenter design and development. As workloads grow more complex and resource-intensive, operators face mounting challenges related to datacenter performance, reliability, and security. If workload demands can’t be consistently met, infrastructure will be unable to scale without disruption. In our latest LinkedIn Live panel discussion , Lattice experts and Bob O’Donnell from TECHnalysis Research explored the increasingly c...

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...

现代交通的演进正加速推动对控制系统的需求——这类系统不仅要快速精准，还需具备较高的适应性与效率。莱迪思FPGA以低功耗、可编程逻辑为各行业工程师赋能，支持实时数据处理、智能系统协同及硬件的快速适配。其中一个突出应用便是超级高铁这一未来交通概念。 Swissloop是一个由苏黎世联邦理工学院支持的学生主导项目，处于超级高铁研发的前沿。该团队专注于提升这种新型交通方式的实际应用潜力，既参与过最初的SpaceX超级高铁竞赛，也投身于其后续赛事“欧洲超级高铁周（EHW）”，并在这些赛事中凭借工程成就广受认可。2023年，Swissloop在爱丁堡举办的欧洲超级高铁周上斩获5项大奖；2024年，该团队首次展示了（超级高铁的）真空兼容性与客运能力，再获3项奖项，并取得总体第三名的成绩。 这些原型的核心是控制架构——而该架构的核心则是莱迪思MachXO3™ FPGA。 2024年苏黎世欧洲超级高铁周（EHW）上的Swissloop原型机 Swissloop 2024年的原型机“Sarah Spring...

过去，人机界面（HMI）相对简单，主要由按钮、旋钮、操纵杆和静态显示屏组成，用于控制基本功能。如今，HMI已演变为复杂的、具备情境感知能力的系统，成为用户与日益智能的机器之间的主要连接桥梁。无论是嵌入在车辆、工业设备、消费电子还是智能基础设施中，现代HMI都必须处理越来越多的任务，包括实时数据可视化、语音交互、生物识别认证以及基于人工智能的自动化。这些系统需要做到直观、响应迅速且安全可靠，同时还要在严格的功耗、空间和性能限制下运行。 这种演变的产物是一种动态、多模态的HMI平台，由先进的软硬件解决方案驱动，例如莱迪思的FPGA及sensAI™解决方案集合。 向自适应人机界面的转变 随着数字系统变得更加智能且互联互通，人机界面必须随之演进以跟上步伐。这些界面不再只是控制面板，而是能实时解读用户意图和系统情境的智能网关。无论是在车辆、工厂还是智能家居中，现代HMI都必须在处理日益复杂的数据和功能的同时，提供无缝、直观的交互体验。 这一转变需要新一代软硬件解决方案，以支持实时响应性、适应性和低功耗。在许多情况下，HMI必须实时处理来自多种传感器的数据，包括摄像头、运动探测器、麦...

设计现代嵌入式系统，往往意味着要在诸多严苛限制下开展工作——空间有限、功耗预算紧张、性能需求却不断攀升。无论是为更智能的工厂自动化提供支持、增强车载智能，还是在网络边缘端实现低功耗AI，开发者都需要灵活、高效且安全的可编程逻辑解决方案。 正因如此，我们满怀期待地宣布扩展我们的小型FPGA产品组合，为莱迪思Certus™-NX和MachXO5™-NX系列增添新成员。这些新器件让通用及安全控制应用的可选方案数量翻倍，在行业最小的尺寸中，实现更高的I/O密度、更多的封装选择以及更先进的可靠性功能。 这些FPGA基于屡获殊荣的莱迪思Nexus™平台打造，在保持低功耗与高性能的同时，为开发者提供更强的架构灵活性和系统级集成能力。 莱迪思小型FPGA产品组合有哪些新突破？ 随着Certus™-NX和MachXO5™-NX系列新器件的加入，莱迪思推出的全新器件选项为在空间和功耗受限环境中开发的工程师带来更高的灵活性、性能与集成度。 新器件具有以下优势： 更丰富的器件选择 更多...

网络安全最佳实践可能随时发生变化。为了帮助我们的客户跟上这一瞬息万变的领域，莱迪思定期举办安全研讨会，组织安全和FPGA专家深入探讨通信、计算、工业、汽车和消费市场的最新安全趋势、法规和实施。我们的目标是提供安全领域相关的见解、真实的市场信号以及对今后发展的认识，这对于正在构建、部署或管理可信系统的开发者尤其重要。 在最新的安全研讨会上，莱迪思安全专家全面概述了CES、MWC、Embedded World和NVIDIA GTC等重大行业活动中出现的全球最新安全趋势。这些活动就如何采取灵活的方法满足信任、零信任架构、后量子加密（PQC）和网络弹性等领域的需求提供了独特的视角。 趋势 1：人工智能安全需求与功能 不出所料，人工智能（AI）仍是网络空间和展会上的热门话题，而用户之间日益增长的信任感则是这一切的核心。虽然人工智能模型可以提高商业环境中的效率和生产力，简化日常任务（例如驾驶），但它们也为恶意行为者提供了类似的优势。此外，人工智能越深入企业系统，攻击面就越大。 为了充分应对这一日益严峻的风险，业界需要从两个角度来处理人工智能安全问题： 将人工智能应用于安全领域：我们...