Peking University

Yachen Xiang

Peng Huang

Xiaoyan Liu

Jinfeng Kang

Runze Han

A spiking convolutional neural network based on a FLASH storage and computing array, including: a sampling module, a FLASH-based storage and computing array and a corresponding neuron module, and a counter module; the sampling module is used to sample an input image to obtain an input spike; the FLASH-based storage and computing array stores a weight matrix, and is used to perform a vector matrix multiplying operation on the input spike and the weight matrix, and an operation result is output in a form of current; the neuron module is used to integrate the operation result of the FLASH-based storage and computing array so as to generate an output spike; the counter module is used to count a number of spikes generated by the neuron module of an output layer, and determine the number of spikes of the neuron module with a largest number of spikes as a recognition result.

Spiking convolutional neural network based on flash storage and computing array

A system and a method for compressing an image based on a FLASH in-memory computing array are provided. The system includes: a convolutional neural network for encoding of the FLASH in-memory computing array, a convolutional neural network for decoding based on the FLASH in-memory computing array, and a quantization module; the convolutional neural network for encoding based on the FLASH in-memory computing array is configured to encode an original image to obtain a feature image; the quantization module is configured to quantize the feature image to obtain a quantized image; the convolutional neural network for decoding based on the FLASH in-memory computing array is configured to decode the quantized image to obtain a compressed image.

Growing community of inventors

Beijing, China

Yachen Xiang