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yexuqing木虫之王 (文学泰斗)
太阳系系主任
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[交流]
融合忆阻器和数字内存计算处理助力高效边缘计算
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融合忆阻器和数字内存计算处理助力高效边缘计算 ▲ 作者：TAI-HAO WEN, JE-MIN HUNG, WEI-HSING HUANG, CHUAN-JIA JHANG, YUN-CHEN LO, HUNG-HSI HSU, ET AL. ▲ 链接： https://www.science.org/doi/10.1126/science.adf5538 ▲ 摘要： 人工智能（AI）边缘设备更倾向于采用高容量非易失性内存计算（CIM）来实现高能效和足够准确的快速唤醒响应。大多数先前的工作要么依据基于忆阻器的CIM，但因其耐用性有限而遭受精度损失且不支持训练；要么依据数字静态随机存取存储器（SRAM）的CIM，但受限于大面积制造需求和易失性存储。 研究组报道了一种使用忆阻器-SRAM CIM融合方案的AI边缘处理器，可同时利用数字SRAM CIM的高精度和电阻式随机存取存储器忆阻器CIM的高能效和存储密度。这也使自适应本地训练能够适应个性化特征和用户环境。 该融合处理器实现了高CIM容量、短唤醒-响应延迟（392微秒）、高峰值能效（77.64 TOPS/W）和稳健的精度（精度损失<0.5%）。这项工作表明，忆阻器技术已经超越了实验室开发阶段，现已具备用于AI边缘处理器的可制造性。 ▲ Abstract： Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based CIMs, which suffer from accuracy loss and do not support training as a result of limited endurance, or digital static random-access memory (SRAM)–based CIMs, which suffer from large area requirements and volatile storage. We report an AI edge processor that uses a memristor-SRAM CIM-fusion scheme to simultaneously exploit the high accuracy of the digital SRAM CIM and the high energy-efficiency and storage density of the resistive random-access memory memristor CIM. This also enables adaptive local training to accommodate personalized characterization and user environment. The fusion processor achieved high CIM capacity, short wakeup-to-response latency (392 microseconds), high peak energy efficiency (77.64 teraoperations per second per watt), and robust accuracy (<0.5% accuracy loss). This work demonstrates that memristor technology has moved beyond in-lab development stages and now has manufacturability for AI edge processors. |
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