{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"07d591c2-0f4c-45ac-a1d7-103c4807db8f","title":"数字经济：内涵与路径","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787521745023","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"07d591c2-0f4c-45ac-a1d7-103c4807db8f","id":"07d591c2-0f4c-45ac-a1d7-103c4807db8f","uuid":"07d591c2-0f4c-45ac-a1d7-103c4807db8f","title":"数字经济：内涵与路径","normalized_title":"数字经济内涵与路径","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787521745023","isbns":["9787521745023"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":361,"navigation_url":"https://workshop.closeclaw.top/ai/book/07d591c2-0f4c-45ac-a1d7-103c4807db8f","title_url":"https://workshop.closeclaw.top/ai/title/%E6%95%B0%E5%AD%97%E7%BB%8F%E6%B5%8E%EF%BC%9A%E5%86%85%E6%B6%B5%E4%B8%8E%E8%B7%AF%E5%BE%84","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/07d591c2-0f4c-45ac-a1d7-103c4807db8f/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/07d591c2-0f4c-45ac-a1d7-103c4807db8f/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/07d591c2-0f4c-45ac-a1d7-103c4807db8f","page_url":"https://workshop.closeclaw.top/api/ai/books/07d591c2-0f4c-45ac-a1d7-103c4807db8f/page","search_url":"https://workshop.closeclaw.top/api/ai/books/07d591c2-0f4c-45ac-a1d7-103c4807db8f/search"},"page":{"printed_page":105,"pdf_page":104,"physical_index":104,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"f3c01c9eff089e60f8b7408d9ad3a9d75dc704bc89777a6c5f7b5e5400c967cf"},"chapter":"第三章","content":"除了上述神经形态芯片外，还有支持神经网络和深度学习的非神经形态芯片，如寒武纪芯片、嵌入式智能芯片图形处理器（GraphicProcessing Unit，GPU）、现场可编程逻辑门阵列（Field ProgrammableGate Array，FPGA）、专用集成电路（Application Specific IntegratedCircuit，ASIC）等。\n\n此外，类脑计算芯片的出现也为类脑计算系统的搭建提供了可能。构建海量类脑计算芯片的阵列集成系统，是目前国际通行的大规模类脑计算系统的技术路线，例如美国、德国、英国分别基于各自的类脑计算芯片构建了类脑计算系统。<sup>37</sup> 清华大学类脑计算中心基于天机类脑计算芯片研制成功国内首台通用类脑计算原型系统。2020年10月14日，清华大学计算机系张悠慧团队和精仪系施路平团队与合作者在《自然》杂志发表题为《一种类脑计算系统层次结构》的论文，填补了类脑计算系统领域完备性理论与相应的类脑计算系统层次结构方面的空白。<sup>38</sup>\n\n除了基于硅技术的类脑计算芯片外，还有一部分类脑计算芯片基于新型纳米器件和技术，例如忆阻器件阵列。忆阻器作为新兴微电子器件，也是目前人工突触的研究重心。记忆功能是忆阻器的重要特征，表现为能够记忆流经它的电荷数量，这直接影响器件的存储能力，让其成为能够促进类脑智能方面的极具潜力的基础元件。<sup>39</sup> 2020年，清华大学吴华强教授团队提出了一种基于全硬件忆阻器实现的卷积神经网络系统，该忆阻器CNN系统能够适应设备缺陷，实现了存算一体结构，并具有良好的可拓展性，与Teala V100 GPU相比，功耗效率提高了2个数量级，性能密度提高了1个数量级。<sup>40</sup>\n\n（三）算力网络\n\n1.概念与发展历程\n\n105","previous_page":104,"next_page":106}