Memristive Monte Carlo DropConnect交叉杆阵列是通过设备和算法共同设计实现的
Do Hoon Kim1, Woon Hyung Cheong2, Hanchan Song1
1Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. km.kim@kaist.ac.kr.
Materials horizons
|June 25, 2024
概括
我们使用Memristive Monte Carlo DropConnect (MC-DC) 开发了节能硬件,用于神经形态计算. 这种联合设计方法优化了硬件算法,提高了神经网络的性能.
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
- 神经科学是一个神经科学.
背景情况:
- 神经形态计算算法越来越复杂,需要专门的硬件来高效地实现.
- 设备和算法的共同设计为创建适合复杂计算任务的节能硬件提供了一条途径.
- 现有的硬件解决方案在满足先进的神经形态算法的需求方面面临挑战.
研究的目的:
- 通过硬件算法联合设计策略开发的一种令人难忘的蒙特卡洛DropConnect (MC-DC) 交叉杆阵列.
- 通过使用特定的memristor设备来证明随机切换和模拟内存特征的集成.
- 在共同设计的硬件上展示MC-DC神经网络的成功实施和性能提升.
主要方法:
- 开发了一个使用基于Ag的扩散选择器和基于Ru的电化学金属化 (ECM) 记忆器的记忆交叉阵列.
- 将设备集成到一个单选器单记忆器 (1S1M) 结构中,实现与工作电压和电流的良好匹配.
- 在制造的硬件上实现了蒙特卡洛DropConnect (MC-DC) 算法,使得随机读取和模拟编程成为可能.
主要成果:
- 在集成的记忆式横杆阵列上成功演示了MC-DC操作.
- 实现了随机切换 (通过选择器) 和模拟内存 (通过memristors) 功能.
- 修改了基于选择器控制的切换极性的MC-DC算法,从而提高了网络性能.
结论:
- 硬件-算法联合设计方法对于开发节能神经形态计算硬件是有效的.
- 记忆设备,特别是基于Ag/Ru的1S1M结构,适合实施MC-DC.等复杂算法.
- 了解和调整算法以适应特定硬件特征,如切换极性,可以显著提高神经网络的性能.
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