Fast and Accurate DNN Performance Estimation across Diverse Hardware Platforms

Performance modeling is an important tool for many purposes such as designing hardware accelerators, improving scheduling, optimizing system parameters, procuring new hardware, etc. This paper provides a new methodology for constructing performance models for Deep Neural Networks (DNNs), a popular machine learning workload. Prior works require running DNNs on existing hardware, which may not be available, or simulating the computation on futuristic hardware, which is slow and not scalable. We instead take an analytical approach based on analyzing the raw operations within DNN algorithms, which allows us to estimate performance across any hardware, even hardware that is in the process of being designed. Evaluations show our approach is fast and gives a good first order approximation ($\pm 10-15 \%$ accuracy) across many DNNs and hardware platforms including GPUs, CPUs, and a futuristic Processing In Memory (PIM) accelerator called BLIMP.