可编程逻辑（FPGA、 CPLD…）

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....

That question opens Lattice Semiconductor’s recent Security Seminar, and it becomes more urgent as humanoids move beyond research environments and begin operating around and interacting with people. Mechanical safeguards and functional safety standards address only part of the risk. When control systems, firmware updates, or data paths can be compromised, security directly determines physical safety. In this seminar, experts from Lattice Semiconductor, SEALSQ, and Promwad examine the real...

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...

Modern defense platforms are caught in a familiar bind. Many of the mission computers still in service today were designed decades ago, and in many cases, they are doing exactly what they were built to do. They rely on interfaces like MIL-STD-1553 and ARINC 429 because those standards have proven reliable and predictable over time. They are deeply embedded across airborne, ground, and naval platforms, and replacing them is far from simple. What has changed is everything around them. New sensors...

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...

The humanoid robotics market is moving quickly from concept to commercial reality. Work that once belonged in research labs is now appearing in factories, warehouses, and service environments due to major improvements in sensing, actuation, and edge intelligence. As these systems take on more complex workloads, developers must deliver dense sensor fusion, sub microsecond motor control loops, and real time perception within tight power and thermal limits. The central question is no longer whethe...

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...

近来，量子计算领域取得了一系列最新进展，后量子加密（PQC）比以往任何时候都更加必要。各行业的开发者迫切需要加强其计算生态系统，以应对量子攻击带来的加剧风险和未知威胁能力。而目前的挑战在于：尚未有一个标准、全面的模型来确保后量子时代的安全。 传统上，开发者能够基于共同的经验制定出标准和最佳实践。但随着量子计算能力的快速发展，他们也难以享受这种便利了，必须找到抵御量子风险的方法，同时又不能对其安全基础设施的长期可行性带来影响。 在我们最新的安全研讨会上，来自莱迪思、PQShield、Quside和Secure-IC的安全专家探讨了不断发展的后量子加密（PQC）需求，以及采用软硬件协同设计方法来满足这些安全需求的重要性。 是什么推动着后量子加密标准的不断发展？ 随着量子领域的持续发展，各种后量子加密标准和指南应运而生。其中最受关注的当属商业国家安全算法套件2.0（CNSA 2.0）——这是美国国家安全局（NSA）发布的一项规定，强制要求使用更强大的后量子加密算法，如Kyber、Dilithium、LMS和XMSS。 尽管CNSA 2.0是后量子加密（PQC）标准...