通过集成闪光突触阵列和超的神经元,重新配置的神经形态计算块
Dongseok Kwon1, Sung Yun Woo2, Kyu-Ho Lee1
1Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
Science advances
|July 19, 2023
概括
我们开发了一个新的神经形态计算 (NC) 块,使用闪存突触和正反 (PF) 神经元. 这种高效和精确的NC块展示了先进AI硬件的巨大潜力.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能 硬件 硬件
- 固态电子 固态电子
背景情况:
- 传统的·诺伊曼架构在处理复杂的人工智能任务时面临局限性.
- 神经形态计算 (NC) 通过模仿生物神经系统提供了一个有希望的替代方案.
- 现有的NC设计往往在效率和可扩展性方面扎.
研究的目的:
- 提出并实验证明一个可重新配置的神经形态计算块.
- 将闪光型突触阵列和正反 (PF) 神经元设备与CMOS电路集成.
- 在能源效率和精度方面评估系统的性能.
主要方法:
- 在单个基板上整合闪光突触阵列和PF神经元设备的新型NC块的制造.
- 使用闪存的电导度调制来轻松校准输出信号.
- 利用PF神经元设备的超急切换来减少模拟到数字转换的开销.
主要成果:
- 拟议的NC区块实现了37.9 TOPS/W的高能效.
- 在CIFAR-10图像分类中,已证明91.80%的高精度.
- 与传统设计相比,高空面积显著减少.
结论:
- 集成的NC块显示了高效和高性能AI系统的高工程潜力.
- 闪光突触和PF神经元的组合为神经形态硬件提供了一个可扩展的解决方案.
- 这项工作为下一代人工智能加速器铺平了道路.
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