This repository contains a collection of scripts/tools for measuring the performance of two typical edge applications: video transcoding and deep learning inference on SoC Clusters and traditional Intel-CPU/NVIDIA GPU server.
For artifact evaluation, checkout exp/ for raw data, post-processed data, and scripts for drawing all figures in our paper.
All binaries/models/videos can be downloaded through Google Drive.
Related Docker image:
piaoliangkb/ffmpeg:nvidia-4.4
Models: ResNet-50, ResNet-152, YOLOv5x, BERT
Software: TVM/TensorFlow (Intel CPU), TensorRT (NVIDIA GPU), TFLite/MNN (SoC CPU/GPU/DSP)
We selected 6 video from vbench in the video transcoding benchmark.
Subtasks:
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Live streaming transcoding
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Archive transcoding
Software:
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Intel CPU / NVIDIA GPU: FFmpeg (with libx264/NVENC/NVDEC support)
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SoC Cluster: cross-compiled FFmpeg for Android with libx264 support / LiTr (developed by LinkedIn)
Two tasks:
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Live streaming transcoding
- Hardware: SoC CPU, SoC Hardware Codec
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Deep learning inference
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Models: ResNet-50, YOLOv5x
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Hardware: SoC CPU/GPU/DSP
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Tests were performed on 6 Snapdragon SoC models released within 2017 - 2022, containing Qualcomm Snapdragon 835, 845, 855, 865, 888, and 8+ Gen 1.