The future data-centric world demands edge intelligence (EI) - the ability to analyze data locally and to decide on a course of action autonomously. Challenges with Moore's Law scaling and limitations of von Neumann computing architectures are limiting the performance and energy efficiency of conventional electronics. Promising new discoveries of advanced CMOS-compatible HfO2-based ferroelectric devices open the door for FerroElectronics; electronics based on ferroelectric building blocks integrated on advanced CMOS technology nodes. It will enable much needed improvement in computing capabilities making EI a reality. In-memory computing in data-flow architectures is at the core of FerroElectronics. This approach will enable building 1000X more compute-energy-efficient small-system AI engines needed for EI. Smart edge intelligent IoT devices enable new applications, for example, micro Drones(uDrones), that demand higher performance to support local embedded intelligence, real-time learning, and autonomy. They will drive the next phase of growth in the semiconductor industry.

中文翻译：

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FerroElectronics 的边缘智能


未来以数据为中心的世界需要边缘智能（EI）——本地分析数据并自主决定行动方案的能力。摩尔定律扩展的挑战和冯·诺依曼计算架构的局限性正在限制传统电子产品的性能和能源效率。先进的 CMOS 兼容 HfO2 基铁电器件的新发现有望为 FerroElectronics 打开大门；基于集成在先进 CMOS 技术节点上的铁电构件的电子器件。它将实现计算能力急需的改进，使 EI 成为现实。数据流架构中的内存计算是 FerroElectronics 的核心。这种方法将能够构建 EI 所需的计算能效提高 1000 倍的小型系统 AI 引擎。智能边缘智能物联网设备支持新的应用，例如微型无人机（uDrones），这些应用需要更高的性能来支持本地嵌入式智能、实时学习和自治。它们将推动半导体行业下一阶段的增长。