{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":26,"pdf_page":42,"physical_index":42,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"4a471dae5f4e72c81cd3ec208359e98d346b5448be1af31b524dd15b27e5bf87"},"chapter":"第2章","content":"AI芯片目前有三个技术路径，通用的GPU（既能作为图形处理器引爆游戏业务，又能渗透数据中心横扫训练端）、可编程的FPGA（适用于迭代升级，各类场景化应用前景超大）以及专业的ASIC（叩开终端AI的大门）。其中，英伟达、英特尔两大传统芯片巨头在三大路径，特别是通用芯片和半定制芯片都有布局，掌握强大的先发优势，在数据中心、汽车等重要蓝海布局扎实；ASIC方面，谷歌从TPU出发开源生态进行布局，且二代TPU展露了训练端芯片市场的野心，且ASIC定制化的特点有效规避了传统巨头的垄断局面，有着可靠健康的发展路线。表2-1总结了目前几个主流的AI芯片厂商。\n\n英伟达是GPU的行业领袖。GPU是目前深度学习领域的主流芯片，拥有强大的并行计算力。而另一个老牌芯片巨头英特尔则是通过大举收购进入FPGA人工智能芯片领域的。谷歌的TPU是专门为其深度学习算法TensorFlow设计的，TPU也用在了AlphaGo系统中。2017年发布的第二代CloudTPU理论算力达到了180TFlops，能够对机器学习模型的训练和运行带来显著的加速效果。类脑芯片是一种基于神经形态工程，借鉴人脑信息处理方式，具有学习能力的超低功耗芯片。IBM从2008年开始模拟人类大脑的芯片项目。苹果正在研发一款名为“苹果神经引擎(AppleNeuralEngine）”的专用芯片。该芯片定位于本地设备的AI任务处理，把面部识别、语音识别等任务集中到AI模块上，提升AI算法效率，未来嵌入苹果的终端设备中。\n\n人工智能与大数据技术导论\n\n26","previous_page":25,"next_page":27}