The disclosure discloses a neural-
network computing system. The
system includes: an I / O interface, which is used for I / O of data; a memory, which is used for temporarily storing a multi-layer artificial-neural-
network model and
neuron data; an artificial-neural-network
chip, which is used for executing multi-layer artificial-neural-network operation and a back-propagation training
algorithm thereof, wherein data and a program from a
central processing unit (CPU) are accepted, and the above-mentioned multi-layer artificial-neural-network operation and the back-propagation training
algorithm thereof are executed; the
central processing unit CPU, which is used for data transportation and starting / stopping control of the artificial-neural-network
chip, is used as an interface of the artificial-neural-network
chip and external control, and receives results after execution of the artificial-neural-network chip. The disclosure also discloses a method of applying the above-mentioned
system forartificial-neural-network compression encoding. According to the system, a model size of an
artificial neural network can be effectively reduced,
data processing speed of the
artificial neural network can be increased,
power consumption can be effectively reduced, and a
resource utilization rate can be increased.