Abstract: A spiking neural network acceleration method includes constructing an approximate computation model according to a spiking neuron model and a spiking coding mode. The approximate computation model utilizes the characteristic that spiking frequency domain coding ignores the time semantics of a spiking sequence, compresses the distribution of spiking signals on a time step, and greatly reduces a spiking routing process and a subsequent neural computation process. The time-driven accelerator replans the computation process of the spiking neural network, sets a deduplication queue and a bitmap to solve the problem of spiking jitter, and realizes efficient support on the inhibitory spiking neural network.

Abstract: This application discloses a method for network secure storage. The method includes: receiving a client request to create a window; creating a window in response to the client request; allocating a storage area for the window; allocating a rendering process to the window; and providing, to the rendering process, a storage service using the storage area.

Abstract: This application discloses a method for network secure storage. The method includes: receiving a client request to create a window; creating a window in response to the client request; allocating a storage area for the window; allocating a rendering process to the window; and providing, to the rendering process, a storage service using the storage area.