STEM与日常科技·英语30篇（3）
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GPU Tensor Cores and Matrix Multiplication
GPU张量核心与矩阵乘
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GPUs contain special units called tensor cores designed exclusively for multiplying huge matrices in one hardware step.GPU 包含专为一步硬件运算大规模矩阵乘法而设计的张量核心。
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While CPUs handle tasks sequentially, tensor cores crunch thousands of multiply-add operations simultaneously.CPU 顺序处理任务，而张量核心可同时执行数千次乘加运算。
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This acceleration powers real-time video translation, where frames flow as pixel matrices needing constant transformation.这种加速能力支撑实时视频翻译——画面以像素矩阵形式连续流动，需持续变换。
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Training a language model involves billions of matrix updates per second—tensor cores make that physically possible.训练语言模型每秒需完成数十亿次矩阵更新，张量核心使其在物理上成为可能。
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Even smartphone GPUs now include tiny tensor units to run face filters or AR overlays without draining the battery.就连智能手机 GPU 也集成了微型张量单元，可在不耗尽电量的情况下运行人脸滤镜或 AR 叠加效果。
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The math behind image recognition, speech synthesis, and weather simulation all rely on repeated, fast matrix math.图像识别、语音合成和天气模拟背后的数学，全都依赖快速、重复的矩阵运算。
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Tensor cores don’t replace CPUs—they complement them by offloading math-heavy workloads with predictable patterns.张量核心并非取代 CPU，而是通过卸载具有规律性的密集数学计算任务来与之协同。
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Their efficiency explains why AI moved from labs to laptops, phones, and embedded devices within a single decade.其高效性解释了 AI 为何能在短短十年内从实验室走向笔记本电脑、手机及嵌入式设备。