A CMOS analog neuro-chip with stochastic learning and multilevel weight storage

被引:0
|
作者
Conti, M
Guaitini, G
Turchetti, C
机构
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A CMOS analog neural network is described in this paper. The chip, fabricated in a 1.0 mu m CMOS technology, has a 2-dimension input vector, 16 neurons, 112 weights, each one with a multilevel storage circuit, 28 internal uncorrelated noise sources, a random weight change learning algorithm implemented on-chip, weight access capability.
引用
收藏
页码:1844 / 1847
页数:4
相关论文
共 33 条
  • [31] VHDL-AMS behavioral model of an analog neural networks based on a fully parallel weight perturbation algorithm using incremental On-Chip learning
    Michel, J
    Herve, Y
    Proceedings of the IEEE-ISIE 2004, Vols 1 and 2, 2004, : 211 - 216
  • [32] A 288μW Programmable Deep-Learning Processor with 270KB On-Chip Weight Storage Using Non-Uniform Memory Hierarchy for Mobile Intelligence
    Bang, Suyoung
    Wang, Jingcheng
    Li, Ziyun
    Gao, Cao
    Kim, Yejoong
    Dong, Qing
    Chen, Yen-Po
    Fick, Laura
    Sun, Xun
    Dreslinski, Ron
    Mudge, Trevor
    Kim, Hun Seok
    Blaauw, David
    Sylvester, Dennis
    2017 IEEE INTERNATIONAL SOLID-STATE CIRCUITS CONFERENCE (ISSCC), 2017, : 250 - 250
  • [33] A 3.43TOPS/W 48.9pJ/Pixel 50.1nJ/Classification 512 Analog Neuron Sparse Coding Neural Network with On-Chip Learning and Classification in 40nm CMOS
    Buhler, Fred N.
    Brown, Peter
    Li, Jiabo
    Chen, Thomas
    Zhang, Zhengya
    Flynn, Michael P.
    2017 SYMPOSIUM ON VLSI CIRCUITS, 2017, : C30 - C31