材料到系统级对CMOS集成RRAM的基准测试,具有超快速切换,用于低功耗的芯片上学习
Minhaz Abedin1,2, Nanbo Gong3, Karsten Beckmann1,4
1University at Albany, College of Nanotechnology, Science and Engineering, Albany, NY, 12203, USA.
Scientific reports
|September 11, 2023
概括
这项研究表明,氧化物 (TaOx) 电阻随机存取存储器 (RRAM) 设备为人工智能 (AI) 训练提供了低功耗的解决方案,与氧化物 (HfOx) RRAM相比,可实现高精度,能耗降低.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 最先进的人工智能 (AI) 模型耗电,需要低功耗的培训解决方案.
- 非易失性内存硬件,特别是电阻随机访问内存 (RRAM),为节能AI提供了一个有希望的途径.
- RRAM 切换特性,如离散导电量状态和可变性,极大地影响模拟硬件的训练准确性和功耗.
研究的目的:
- 调查RRAM交换特性对模拟AI硬件的物质依赖性.
- 在一晶体管一RAM (1T1R) 配置中制造和表征氧化物 (TaOx) 和氧化物 (HfOx) RRAM设备.
- 在模拟内存训练算法中评估这些RRAM设备的性能.
主要方法:
- 使用 65 nm CMOS 工艺制造 TaOx 和 HfOx RRAM 设备.
- 模拟开关性能评估,使用不同的电成型合规电流 (200-500μA).
- 超短脉冲宽度 (300 psi) 模拟切换测试和系统级训练准确性基准测试使用内存算法.
主要成果:
- 无论是TaOx还是HfOx RRAM设备,都表现出超过20个不同的导电量状态.
- 陶设备的导电率比HfOx低10倍,显著降低了功耗.
- 使用TaOx 1T1R细胞进行模拟内存训练,获得了96.4%的准确率,接近97%的浮点基线,而HfOx获得了90.5%的准确率.
结论:
- 优化的电成形和切换参数使得高数量的RRAM状态具有低导电.
- 陶RRAM是用于低功耗模拟AI训练的优质材料,因为其导电率较低,准确度高.
- 这项研究为模拟人工智能硬件的材料工程提供了一个框架,用于节能培训.
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