基于金属氧化物记忆器的综合神经形态网络的培训和运行
M Prezioso1, F Merrikh-Bayat1, B D Hoskins1
1Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, California 93106, USA.
Nature
|May 8, 2015
概括
研究人员为神经形态网络开发了无晶体管的金属氧化物memristor横杆. 这一突破使得可扩展,高效的人工智能硬件成为可能,在简单的神经网络中展示了成功的图像分类.
科学领域:
- 神经形态工程的神经形态工程
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 在硬件中实现人类大脑皮层复杂性 (10^14突触) 是由于规模和功率而具有挑战性的.
- 互补的金属氧化物半导体 (CMOS) 和memristor电路为更快,更低功率的神经形态网络提供了途径.
- 以前的方法使用了离散的memristor或相变memristive设备,需要额外的晶体管,阻碍了可扩展性.
研究的目的:
- 实验性地实现无晶体管的金属氧化物memristor交叉杆,用于可扩展的神经形态计算.
- 用这些memristor横杆来证明集成神经网络的可行性.
- 在像图像分类这样的实际应用中评估这些网络的性能.
主要方法:
- 制造具有低设备变化的金属氧化物memristor横杆.
- 这些横杆与CMOS电路的集成,用于单层感知器.
- 在现场训练网络使用三角规则算法进行图像分类.
主要成果:
- 成功实施了无晶体管的金属氧化物记忆器横杆.
- 实现了适合集成神经网络操作的低设备可变性.
- 使用训练有素的感知器,证明了将3x3像素图像完美分类为三个类别.
结论:
- 无晶体管的金属氧化物memristor交叉杆是构建可扩展神经形态网络的可行技术.
- 这项工作是实现大规模,高效的人工智能硬件的重要一步.
- 展示的图像分类能力突显了在实际AI应用中记忆型横杆的潜力.
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