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  3. Shadernn: A Lightweight And Efficient Inference Engine For Real-time Applications On Mobile Gpus.
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  3. Shadernn: A Lightweight And Efficient Inference Engine For Real-time Applications On Mobile Gpus.

Related Experiment Video

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ShaderNN: A Lightweight and Efficient Inference Engine for Real-time Applications on Mobile GPUs.

Jing Xie1,2, Yuzhong Yan2, Abhishek Saxena2

  • 1Department of Electrical and Computer Engineering, University of Maryland at College Park, 8223 Paint Branch Dr, College Park, MD, 20740, USA.

Neurocomputing
|January 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Shader Neural Network (ShaderNN) is a new OpenGL-based framework for efficient deep learning inference on mobile devices. It minimizes data movement and boosts performance, outperforming existing solutions like TensorFlow-Lite.

Keywords:
00001111Deep learningInferenceShader

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Mobile Computing

Background:

  • Deep neural network inference on mobile devices faces challenges due to limited resources (computation, power, memory).
  • Real-time applications require minimized data movement and increased data locality for efficient inference.
  • Existing inference engines often involve costly data transfers between CPU and GPU.

Purpose of the Study:

  • To propose Shader Neural Network (ShaderNN), a fast and power-efficient OpenGL-based inference framework for mobile devices.
  • To address challenges of limited computational capacity, power budget, and data movement in mobile deep learning.
  • To enable seamless integration with real-time graphics and image processing applications.

Main Methods:

  • Developed ShaderNN using OpenGL, leveraging texture-based input/output for zero-copy integration.
  • Utilized fragment shaders for neural network inference operators, particularly for smaller models.
  • Implemented a hybrid compute and fragment shader approach with layer-level shader selection for performance optimization.
  • Employed OpenGL features like normalization, interpolation, and texture padding to enhance performance.

    • Main Results:

    • ShaderNN demonstrated superior performance compared to TensorFlow-Lite on mobile devices with Qualcomm and MediaTek chips.
    • The framework achieved efficient, power-saving inference by minimizing data transfers between CPU and GPU.
    • A case study confirmed ShaderNN's usability and seamless integration within an Android media processing application.

    Conclusions:

    • ShaderNN offers a novel and effective solution for on-device deep learning inference, optimizing for mobile constraints.
    • The texture-based, OpenGL-centric approach provides significant performance and efficiency gains.
    • ShaderNN is a viable and performant alternative for integrating deep learning into mobile applications.