在2D浮式门内存中使用大规模卷积神经网络的门注入模式的8位状态
Yuchen Cai1,2, Jia Yang1,2, Yutang Hou3
1CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing, P. R. China.
Nature communications
|March 19, 2025
概括
新的门注入模式二维浮动门记忆提供高效的神经形态计算. 这些先进的记忆实现了8位状态和稳定性,为大规模的神经网络加速器铺平了道路.
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:
- 人工智能的快速发展需要高效的神经形态计算硬件.
- 现有的二维浮动门记忆在状态数量和稳定性方面存在局限性,这限制了它们的实际应用.
研究的目的:
- 为大规模神经网络加速器引入二维浮动门记忆作为可行的解决方案.
- 为了提高神经形态计算应用程序的内存性能.
主要方法:
- 使用了共平面装置结构设计.
- 实施了双脉冲状态编程策略,以实现多位存储.
- 制造并测试了一系列256个设备,以评估性能和产量.
主要成果:
- 实现了8位状态,间隔大于3V的标准偏差的三倍.
- 经过证明的稳定性超过10,000秒,循环耐力超过105.5.
- 在256个制造的设备中实现了94.9%的设备产量.
- 成功地在9x2数组上进行了实验性图像卷积和内核移植,结果与模拟一致.
- 展示了8位精度固定点神经网络可以达到接近理想值的推理精度.
结论:
- 门注入模式二维浮动门记忆显示了高效神经形态计算硬件的巨大潜力.
- 演示的性能指标和成功的集成验证了它们适用于神经网络加速器的适用性.
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