Optimizing CNNs on Multicores for Scalability, Performance and Goodput

Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object recognition, and natural language processing. CNNs are, however, computationally intensive to train. This paper presents the first characterization of the per- formance optimization opportunities for training CNNs on CPUs. Our characterization includes insights based on the structure of the network itself (i.e., intrinsic arithmetic inten- sity of the convolution and its scalability under parallelism) as well as dynamic properties of its execution (i.e., sparsity of the computation).