Recurrent neural networks (RNNs) are an important class of deep learning (DL) models. Existing DL frameworks have unsatisfying performance for online serving: many RNN models suffer from long serving latency and high cost, preventing their deployment in production. This work characterizes RNN performance and identifies low data reuse as a root cause. We develop novel techniques and an efficient search strategy to squeeze more data reuse out of this intrinsically challenging workload. We build DeepCPU, a fast serving library on CPUs, to integrate these optimizations for efficient RNN computation. Our evaluation on various RNN models shows that DeepCPU improves latency and efficiency by an order of magnitude on CPUs compared with existing DL frameworks such as TensorFlow. It also empowers CPUs to beat GPUs on RNN serving. In production services of Microsoft, DeepCPU transforms many models from non-shippable (due to latency SLA violation) to shippable (well-fitting latency requirements) and saves millions of dollars of infrastructure costs.<\/p>\n","protected":false},"excerpt":{"rendered":"
Recurrent neural networks (RNNs) are an important class of deep learning (DL) models. Existing DL frameworks have unsatisfying performance for online serving: many RNN models suffer from long serving latency and high cost, preventing their deployment in production. This work characterizes RNN performance and identifies low data reuse as a root cause. 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