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ACCELERATING SPARSE MATRIX MULTIPLICATION IN STORAGE CLASS MEMORY-BASED CONVOLUTIONAL NEURAL NETWORK INFERENCE
ACCELERATING SPARSE MATRIX MULTIPLICATION IN STORAGE CLASS MEMORY-BASED CONVOLUTIONAL NEURAL NETWORK INFERENCE
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机译:基于存储类内存的卷积神经网络推论的加速稀疏矩阵乘法
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摘要
Techniques are presented for accelerating in-memory matrix multiplication operations for a convolution neural network (CNN) inference in which the weights of a filter are stored in the memory of a storage class memory device, such as a ReRAM or phase change memory based device. To improve performance for inference operations when filters exhibit sparsity, a zero column index and a zero row index are introduced to account for columns and rows having all zero weight values. These indices can be saved in a register on the memory device and when performing a column/row oriented matrix multiplication, if the zero row/column index indicates that the column/row contains all zero weights, the access of the corresponding bit/word line is skipped as the result will be zero regardless of the input.
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