Slim Embedding Layers for Recurrent Neural Language Models
Table 4: Time Usage Comparison
['Model', 'CPU(seconds)', 'GPU (milliseconds)']
We report the time used on both CPU and GPU. All the computations use 32 bit floating point numbers. On CPU, HashNet is slower than the normal uncompressed model, mainly because of two reasons: 1) The uncompressed model uses optimized matrix multiplication subroutines, 2) The hash function used in HashNet is cheap, but it still has overhead compared with the uncompressed model. The SE model runs faster mainly because it uses matrix multiplication subroutines and has lower time complexity with the help of dynamic programming.