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更新适应干扰的模拟ReRAM交叉阵列用于内存深度学习加速器
Wooseok Choi1, Tommaso Stecconi1, Donato Francesco Falcone1
1IBM Research Europe-Zurich, Rüschlikon, 8803, Switzerland.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 16, 2025
概括
电阻式内存 (ReRAM) 设备通过克服重量更新干扰来实现高效的内存人工智能训练. 这一突破推动了可持续,节能的人工智能加速器的发展.
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
- 人工智能的人工智能
背景情况:
- 具有交叉条数组的电阻式内存 (ReRAM) 对模拟AI加速器有很大的希望,使内存推断和训练成为可能.
- 当前的人工智能加速通常将训练卸载给外部处理器,从而限制了功率效率.
- 内存训练加速对于可持续的AI至关重要,但面临着诸如体重更新障碍等挑战.
研究的目的:
- 为了解决在模拟ReRAM阵列中完全并行更新重量时的重量值干扰的挑战,用于内存训练.
- 介绍一种新的ReRAM设备解决方案,并证明其对无干扰并行重量更新的能力.
主要方法:
- 在HfOx层上使用导电性金属氧化物 (CMO) 的ReRAM设备,在350nm技术上使用纳米级导电丝.
- 使用COMSOL多物理模拟分析了设备干扰耐受性,模拟了导线诱导的热电效应.
- 在后端集成的ReRAM阵列芯片上展示了无干扰的平行重量映射.
主要成果:
- ReRAM设备具有快速 (60 ns) 的非挥发性模拟切换.
- 设备显示出异常的弹性更新干扰,承受超过10万脉冲.
- 在ReRAM阵列芯片上成功展示了无干扰并行重量映射.
结论:
- 开发的ReRAM技术为内存AI训练加速提供了可行的解决方案.
- 对于下一代人工智能硬件来说,设备对干扰的弹性和证明的并行更新能力至关重要.
- 硬件意识的神经网络模拟证实了深度学习加速器中完全并行的重量更新的潜力.
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