论文信息 - A self-learning neural network chip with 125 neurons and 10 K self-organization synapses

A self-learning neural network chip with 125 neurons and 10 K self-organization synapses

The authors propose a neural network chip that can organize the connection weight of each synapse with 125 neurons so that it can learn on chip. The chip employs the mixed design architecture of digital and analog circuits in a 1.0-μm CMOS technology. The chip operates more than 1000 times faster than conventional computers

[1] Lawrence D. Jackel,et al. VLSI implementation of a neural network model , 1988, Computer.

[2] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[3] Sompolinsky,et al. Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[4] S. Kayano,et al. A self-learning neural network chip with 125 neurons and 10 K self-organization synapses , 1990 .

[5] S. Tam,et al. An electrically trainable artificial neural network (ETANN) with 10240 'floating gate' synapses , 1990, International 1989 Joint Conference on Neural Networks.