Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Abstract

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 are hard to run on resource-limited mobile platforms or architecture simulators. To provide a more scalable evaluation environment, we propose a new DNN benchmark suite that can run on any platform that supports CUDA and OpenCL. The proposed benchmark suite 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.

References in corpus (6)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
• Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
• Fathom: Reference Workloads for Modern Deep Learning Methods
• TBD: Benchmarking and Analyzing Deep Neural Network Training

Cited by in corpus (2)

• SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization
• ALTIS: Modernizing GPGPU Benchmarking