评估非易失性基于内存的内存计算加速器的设备电路编码设计的设计空间
Ashwin Sanjay Lele1, Bo Zhang1, Win-San Khwa2
1Corporate Research, TSMC, San Jose, California 95134, United States.
Nano letters
|January 13, 2025
概括
新的非易失性存储器 (NVM) 设备增强了人工智能 (AI) 硬件. 最佳的NVM设备电阻对于准确高效的内存计算 (CIM) 芯片性能至关重要,指导未来的电子硬件创新.
科学领域:
- * 探索用于人工智能 (AI) 应用的先进电子硬件.
- *专注于非易失性内存 (NVM) 设备和内存计算 (CIM) 架构.
背景情况:
- *人工智能 (AI) 算法正在推动电子硬件方面的创新.
- * 非易失性存储器 (NVM) 设备在密度和数据保留方面具有优势.
- *计算在内存 (CIM) 架构将计算与人工智能模型存储集成,以提高能源效率.
研究的目的:
- * 评估NVM设备电阻对CIM芯片准确性和电路性能的影响.
- * 为设备工程师提供建议,以优化设备-电路-系统交互.
- * 审查NVM设备编程中的挑战,并对现有的NVM-CIM芯片进行基准测试.
主要方法:
- *对NVM设备和CIM架构的文献综述.
- *分析建模以确定最佳的设备阻力极限.
- *对最近NVM-CIM芯片性能进行基准测试.
主要成果:
- * 在NVM设备中,高电阻比率和低可变性是最佳性能的优势.
- *NVM设备的低电阻状态受到精度和电路性能要求的限制.
- * 设备电阻直接影响NVM-CIM芯片的性能和设计决策.
结论:
- * 通过优化NVM设备特性,可以实现无摩擦的设备-电路-系统交互.
- *了解阻力极限对于设计高性能NVM-CIM芯片至关重要.
- *未来的研究应该专注于可靠的NVM设备编程和先进的CIM架构.
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