莱迪思博客

The expansion of artificial intelligence (AI) solutions is driving unprecedented change in datacenter infrastructure. These changes are not confined to raw compute power, either: platform complexity, power density, thermal management needs, and security demands all continue to evolve to support high volume, complex AI workloads. In a recent Security Seminar, experts from Lattice, ASPEED, and AMI discussed growing infrastructure demands, their impact on datacenter design, and why strategic, layer...

全球数据中心市场正以惊人速度扩张，预计到 2033 年市场规模将突破 9000 亿美元。这一前所未有的增长由对 AI 计算能力的强劲需求所驱动，进而要求服务器更加强大、更加复杂。 这些服务器历来采用精简、统一、高度集成的架构运行。然而，需求的持续增长催生了更加分散、异构的数据中心基础设施，给运营商的管理与控制带来了更大挑战。 在 Embedded Computing Design 近期主办的网络研讨会上，Lattice 深入探讨了这一日益复杂的趋势、AI 服务器网络面临的不断扩大的安全风险，以及现场可编程门阵列（FPGA）如何为数据中心安全创新提供所需的控制与信任基础。 数据中心解耦驱动复杂性与风险上升 从统一服务器系统向解耦 AI 基础设施的转变，在现代数据中心中以多种形式呈现。一方面，这涉及在同一服务器机架中整合来自多家供应商的组件，包括不同的 CPU、GPU、网卡（NIC）、数据处理器（DPU）、加速器等；另一方面，还需要支持混合模式，即同时利用云端与本地基础设施，使管理层在系统软硬件层面更加碎片化。 这种解耦带来的问题可归结为三大核心运营痛点： 复杂性爆...

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...