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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...

Our digital world is undergoing a profound transformation. Cloud computing, artificial intelligence (AI) and machine learning (ML) workloads, as well as the emergence of quantum computing capabilities, are reshaping how networks must be protected and secured. What’s more, these changes come amid evolving risks & regulations – from the prevalence of “harvest now; decrypt later” attacks, to updated standards like the Commercial National Security Algorithm Suite (CNSA) 2...

Quantum computing is no longer just a concept confined to research labs. Thanks to rapid progress in both hardware and algorithms, the risk to today’s cryptographic systems is steadily increasing. In 2025, Google’s 105-qubit Willow chip and Microsoft’s Majorana 1 processor demonstrated that scalable quantum systems are moving closer to practical reality. Industry experts now predict that quantum computers capable of breaking RSA-2048 encryption could arrive as early as 2030 to ...

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...

Building and maintaining connected digital ecosystems that account for today’s evolving cyber threat landscape requires a degree of hardware-based trust, as software-only security approaches are no longer sufficient to protect complex, distributed systems Luckily, today’s developers can reference a foundational example of hardware-based security that has existed for decades: the Trusted Platform Module (TPM). With over four billion TPM units deployed globally across a wide range of u...

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...

服务器是现代计算基础设施的中坚力量。它们承载着敏感数据、AI模型以及核心工作负载，因此成为愈发复杂网络威胁的主要目标。随着服务器架构日益模块化、分布式发展，且集成多种CPU、网络接口卡、加速器、SCM模块等，加之企业对这些分布式系统的依赖不断加深，保障其安全的复杂性也随之成倍增加。 近期的攻击事件——例如利用Secure Boot漏洞或利用本地部署环境的零日漏洞——充分证明了平台级攻击如何绕过传统软件防线。这些威胁常通过固件植入与持久化攻击路径，悄然突破常规防御体系。相应地，监管框架与行业标准（包括CNSA 2.0、NIST 800-193和欧盟网络弹性法案）正日益要求采用强制的硬件安全措施，比如平台弹性、加密保障与安全生命周期管理。 要满足这些要求并抵御高级威胁，绝非易事。为系统开发者提供强大的硬件解决方案与安全最佳实践，可帮助企业构建具备弹性的服务器架构，从而支持安全、可扩展的计算环境。 服务器级安全面临哪些挑战？ 要打造具备弹性的基础设施，开发者必须解决影响服务器级安全的核心障碍。 这些持续演化的威胁，包括但不限于： ...

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...