Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. Although DNN runtime latency is dictated by model property (e.g., architecture, operations), hardware property (e.g., utilization, throughput), and more importantly, the effective mapping between these two, many existing approaches focus only on optimizing model property such as FLOPS reduction and overlook the mismatch between DNN model and hardware properties. In this work, we show that the mismatch between the varied DNN computation workloads and GPU capacity can cause the idle GPU tail effect, leading to GPU under-utilization and low throughput. As a result, the FLOPs reduction cannot bring effective latency reduction, which causes sub-optimal accuracy versus latency trade-offs. Motivated by this, we propose a GPU runtime-aware DNN optimization methodology to eliminate such GPU tail effect adaptively on GPU platforms. Our methodology can be applied on top of existing SOTA DNN optimization approaches to achieve better latency and accuracy trade-offs. Experiments show 11%-27% latency reduction and 2.5%-4.0% accuracy improvement over several SOTA DNN pruning and NAS methods, respectively
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