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裁剪随机电阻内存以优化模拟AI的优化
Yi Li1,2,3,4,5, Songqi Wang1,2,3,5, Yaping Zhao1
1Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
Nature communications
|January 10, 2026
概括
这项研究介绍了一种新的软硬件联合设计,用于使用电阻记忆神经网络的节能AI. 它显著提高了准确性,减少了能源消耗,克服了模拟计算中的编程障碍.
科学领域:
- 人工智能的人工智能
- 计算机工程 计算机工程
- 材料科学 材料科学 材料科学
背景情况:
- 越来越多的人工智能模型增加了能源需求,促使对高效计算的研究.
- 具有电阻性内存的模拟内存计算提供了一种节能解决方案,但面临着编程和设备挑战.
研究的目的:
- 开发一个软硬件共同设计用于训练电阻记忆神经网络.
- 解决模拟内存计算中的编程挑战和设备非理想性.
- 为了提高人工智能硬件的能源效率和准确性.
主要方法:
- 提出一种软硬件共同设计的方法来训练随机加权的电阻记忆神经网络.
- 使用边缘修剪拓优化来定制网络架构.
- 利用电阻记忆电成形静态度来产生随机权重.
- 在40nm电阻式内存芯片上实现共同设计.
主要成果:
- 获得了17.3% (时尚-MNIST) 和19.9% (口头数字) 的精度改进.
- 在DRIVE.上确保了9.8%的精度回忆AUC改进.
- 在不同任务中减少了高达99.7%的能源消耗.
- 在模拟内存类型中证明了适用性,并可扩展到复杂的模型,如ResNet-50.
结论:
- 拟议的软硬件联合设计有效地训练了节能电阻记忆神经网络.
- 这种方法提高了对设备变化的稳定性,并减少了编程开销.
- 该方法显示了推进低功耗人工智能硬件和模拟计算的巨大潜力.
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