  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
