ONNX Runtime Web整合WebGPU，提升浏览器性能

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微软旗下的ONNX Runtime Web近日宣布，整合了WebGPU技术，旨在提升网络浏览器中复杂机器学习模型的运行性能。虽然这项技术目前处于早期阶段，但Chrome浏览器和Edge浏览器已经开始初步支持。　　ONNX Runtime Web和WebGPU简介　　ONNX Runtime Web：微软推出的JavaScript库，使网络开发人员能够直接在浏览器中部署机器学习模型，并提供基于...

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Web：微软推出的JavaScript库，使网络开发人员能够直接在浏览器中部署机器学习模型，并提供基于硬件加速的多个后端支持。\n　　WebGPU：一种网络应用程序接口（API），为在浏览器中运行的机器学习模型提供硬件加速支持。\n　　解决以往的挑战\n　　以往，由于计算能力有限，尤其是针对大型生成模型等复杂机器学习模型，在网络浏览器中高效运行存在挑战。然而，随着WebGPU的出现，现在可以释放设备GPU的能力来处理这些任务，从而显著提高性能。\n　　微软的表态\n　　微软表示：\n　　高效处理复杂任务：通过WebGPU的先进功能，如计算着色器等，能够更高效地处理更复杂的机器学习工作负载。\n　　降低GPU内存使用率：WebGPU对半精度（FP16）的支持降低了GPU内存的使用率和带宽要求，同时提升了运算速度。\n　　推动高效机器学习应用：WebGPU利用GPU处理并行计算任务的能力，直接在浏览器中推断出更高效、可扩展的机器学习应用。\n　　支持情况\n　　适用于Mac、Windows、ChromeOS的Chrome 113和Edge 113，以及适用于Android的Chrome 121，默认已经包含了WebGPU。开发者可以通过ONNX Runtime Web尝试调用WebGPU加速资源，提升机器学习模型在浏览器中的性能表现。\n　　微软通过整合WebGPU技术，不断推动机器学习在网络浏览器中的发展，为开发人员提供更加强大和高效的工具和平台。这一创新将促进机器学习应用的普及，推动其在互联网时代的广泛应用。","kind":null,"is_publish_news":null,"is_publish_highlight":null,"is_publish_live":null,"is_publish_wemedia":null,"editions":null,"symbols":[],"gpt_button":1},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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