Detailed Characterization of Deep Neural Networks on GPUs and FPGAs
Aajna Karki, Chethan Palangotu Keshava, Spoorthi Mysore Shivakumar, Joshua Skow, Goutam Madhukeshwar Hegde, Hyeran Jeon · 2019
Deep neural networks (DNNs) have been proving the effectiveness in various computing fields. To provide more efficient computing platforms for DNN applications, it is essential to have evaluation environments that include assorted benchmark workloads. Though a few DNN benchmark suites have been recently released, most of them require to install proprietary DNN libraries or resource-intensive DNN frameworks, which can run only on certain architectures. Also, some of the benchmark suites only support a few per-layer functions where the interactions between layers can not be measured. To provide a more scalable evaluation environment, we present a new DNN benchmark suite, Tango, that can run on any platform that supports CUDA and OpenCL. Tango includes the most widely used five convolution neural networks and two recurrent neural networks. We provide in-depth architectural statistics of these networks while running them on an architecture simulator, a server- and a mobile-GPU, and a mobile FPGA.