一个接近值的内存计算内存引擎,用于边缘智能
Linfang Wang1,2, Weizeng Li1,3, Zhidao Zhou1,3
1State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
Nature communications
|July 2, 2025
概括
这项研究介绍了一种用于边缘设备的新型内存计算引擎. 它通过使用内在的memristor变体和先进技术克服了可扩展性挑战,实现了高性能和能源效率.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 电气工程 电气工程
背景情况:
- 非传统的计算范式,如内存计算和接近值计算,为边缘设备提供了更高的能源效率和实时性能.
- 这些范式的可扩展性受到过程变化的阻碍,限制了它们的实际应用.
研究的目的:
- 开发和演示一个可扩展的1Mb,16宏接近值的memristive计算内存引擎.
- 通过创新的电路设计和补偿技术来应对过程变化挑战.
主要方法:
- 使用了具有高电流调制能力 (>120倍电阻比) 的两晶体管-一电阻电池.
- 通过利用内在的memristor变化来缓解晶体管不匹配.
- 实施了电荷堆叠技术,以实现高效的模拟重量和组合操作.
- 引入了一种跨宏混合控制方案,以减少推断能力.
主要成果:
- 制造的芯片在256个输入通道中执行高度并行的模拟计算,变化很小 (2.4%的相对标准偏差).
- 实现了每秒10.49特拉操作的峰值吞吐量.
- 证明了高达每瓦每秒88.51特拉操作的特殊能效.
结论:
- 开发的记忆式内存计算引擎有效地克服了由过程变化引起的可扩展性限制.
- 这项工作为更节能,更高性能边缘计算应用铺平了道路.
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