Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators
Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich, Oliver Bringmann · 2022
We present an automatic methodology to accurately predict the performance of Deep Neural Network (DNN) accelerators using abstract descriptions of accelerator architectures and DNNs with a high degree of flexibility. By mapping partially unrolled neural network layers onto accelerator architectures, we automatically construct an analytical performance model, exploiting the dataflow-driven nature of DNNs that allows us to evaluate only a few loop iterations to determine the performance of a whole DNN layer.