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Neural networks offer high-accuracy solutions to a range of problems, but arecostly to run in production systems because of computational and memoryrequirements during a forward pass. Given a trained network, we propose atechique called Deep Learning Approximation to build a faster network in a tinyfraction of the time required for training by only manipulating the networkstructure and coefficients without requiring re-training or access to thetraining data. Speedup is achieved by by applying a sequential series ofindependent optimizations that reduce the floating-point operations (FLOPs)required to perform a forward pass. First, lossless optimizations are applied,followed by lossy approximations using singular value decomposition (SVD) andlow-rank matrix decomposition. The optimal approximation is chosen by weighingthe relative accuracy loss and FLOP reduction according to a single parameterspecified by the user. On PASCAL VOC 2007 with the YOLO network, we show anend-to-end 2x speedup in a network forward pass with a 5% drop in mAP that canbe re-gained by finetuning.

Deep Learning Approximation: Zero-Shot Neural Network Speedup