记忆器和数字内存计算处理的融合,以实现节能边缘计算
Tai-Hao Wen1,2, Je-Min Hung2, Wei-Hsing Huang2
1Taiwan Semiconductor Manufacturing Company Limited (TSMC), No. 8, Li-Hsin Rd. 6, Hsinchu Science Park, Hsinchu 300, Taiwan, R.O.C.
Science (New York, N.Y.)
|April 26, 2024
概括
本研究介绍了一种用于AI边缘设备的新型memristor-SRAM计算内存 (CIM) 融合方案. 这种方法提高了精度和能源效率,为个性化的AI应用程序提供了设备上的培训.
科学领域:
- 材料科学
- 计算机工程
- 人工智能
背景情况:
- 人工智能边缘设备需要高容量,非易失性内存计算 (CIM) 以提高能效和快速响应.
- 现有的CIM解决方案面临着权衡:基于memristor的CIM具有有限的耐久性和精度,而基于SRAM的CIM则具有波动性并需要大面积.
研究的目的:
- 开发一款超越现有CIM技术局限性的AI边缘处理器.
- 利用memristor和SRAM技术的优势来改善AI边缘计算.
主要方法:
- 设计并实施了一种新的memristor-SRAM CIM融合方案.
- 聚变处理器将SRAM CIM的精度与memristor CIM的能量效率和密度相结合.
- 该系统支持适应性本地培训以实现个性化的AI能力.
主要成果:
- 聚变处理器实现了高CIM容量和392微秒的短唤醒响应延迟.
- 实现了每秒每瓦77.64T的最高能效.
- 证明了不到0.5%的精度损失,并证实了AI边缘处理器的memristor技术的可制造性.
结论:
- 记忆器-SRAM CIM融合方案为AI边缘设备提供了卓越的解决方案,平衡精度,效率和容量.
- 这项技术可以在设备上进行自适应训练,增强个性化和用户体验.
- 记忆器技术现在可以制造,并准备好集成到商业AI边缘处理器中.
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