Core Concepts
TF2AIF is a tool that facilitates the development and deployment of accelerated AI models on diverse hardware platforms across the cloud-edge continuum.
Abstract
The paper presents TF2AIF, a tool that automates the generation of multiple versions of AI models optimized for deployment on a variety of heterogeneous hardware platforms, including x86 CPUs, ARM CPUs, server-class FPGAs, high-end GPUs, mobile GPUs, and embedded SoC FPGAs.
Key highlights:
- TF2AIF supports a wide range of hardware platforms spanning the cloud-edge continuum, addressing the increasing complexity of deploying AI models across diverse infrastructures.
- The tool simplifies the process of model conversion, quantization, and container composition, reducing the time and expertise required from users.
- TF2AIF leverages state-of-the-art AI acceleration frameworks like TensorRT and Vitis AI to maximize the performance of the generated model variants.
- The modular and extensible design of TF2AIF allows easy integration of new hardware platforms and AI frameworks, enabling broader applicability.
- The automated generation of model variants and corresponding client containers facilitates rapid prototyping, testing, and benchmarking, as well as enabling advanced AI-driven inference serving scheduling systems.
The evaluation demonstrates that TF2AIF can efficiently generate 20 deployment-ready model variants across various platforms in just a few minutes. The performance analysis shows significant speedups of up to 7.6x when using the specialized AI frameworks compared to native TensorFlow implementations.
Stats
The time required to generate model variants from the TensorFlow models ranges from 20-40 seconds for the compose step, while the conversion time depends on the model size.
The ALVEO version consistently requires the most time for preparation, due to the Vitis-AI conversion process.
The AGX, ARM, CPU, and GPU implementations achieved average speedups of 5.5x, 2.7x, 3.6x, and 7.6x, respectively, compared to their native TensorFlow counterparts.
Quotes
"TF2AIF fills an identified gap in today's ecosystem and facilitates research on resource management or automated operations, by demanding minimal time or expertise from users."
"TF2AIF markedly reduces the time required to transition from model development to deployment. By automating model conversion and container composition processes, TF2AIF enables rapid and efficient generation of production-ready AI services."