C-GOOD: C-code Generation Framework for Optimized On-device Deep Learning

By Duseok Kang, Euiseok Kim, Inpyo Bae, Bernhard Egger, Soonhoi Ha. 2018

27 citations recorded by the source corpus.

Executing deep learning algorithms on mobile embedded devices is challenging because embedded devices usually have tight constraints on the computational power, memory size, and energy consumption while the resource requirements of deep learning algorithms achieving high accuracy continue to increase. Thus it is typical to use an energy-efficient accelerator such as mobile GPU, DSP array, and customized neural processor chip. Moreover, new deep learning algorithms that aim to balance accuracy, speed, and resource requirements are developed on a deep learning framework such as Caffe[16] and Tensorflow[1] that is assumed to run directly on the target hardware. However, embedded devices may not be able to run those frameworks directly due to hardware limitations or missing OS support. To overcome this difficulty, we develop a deep learning software framework that generates a C code that can be run on any devices. The framework is facilitated with various options for software optimization that can be performed according to the optimization methodology proposed in this paper. Another benefit is that it can generate various styles of C code, tailored for a specific compiler or the accelerator architecture. Experiments on three platforms, NVIDIA Jetson TX2[23], Odroid XU4[10], and SRP (Samsung Reconfigurable Processor)[32], demonstrate the potential of the proposed approach.

Subfields: Computer Vision and Pattern Recognition, Hardware and Architecture, Signal Processing

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Citations in the corpus

A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones (2018, background): “While significant work has been done within academia [24], [25], [26] and industry (e.”
The Deep Learning Compiler: A Comprehensive Survey (2020, methodology): “, C-GOOD [50]) as well as enable execution on remote machines (TVM RPC) and customized accelerators.”
Machine Learning at the Network Edge: A Survey (2019, background): “ applications TensorFlowLite (Google) C++ Java C/C++ Java TensorFlowLite NNAPI computervision[109], speechrecognition[42,1] Caﬀe2 Caﬀe2Go (Facebook) C++ Android iOs NNPack imageanalysis, videoanalysis[53] ApacheMXNet C++ Python R Linux MacOS Windows FullModel objectdetection, recognition[78] CoreML2”