GCV-Turbo: A Domain-Specific Accelerator for End-to-End Acceleration of GNN-based Computer Vision Tasks on FPGA

Core Concepts

GCV-Turbo is a domain-specific accelerator on FPGA that provides end-to-end acceleration for GNN-based computer vision tasks by employing a novel hardware architecture and a customized compiler.

Abstract

The paper introduces GCV-Turbo, a domain-specific accelerator on FPGA for end-to-end acceleration of GNN-based computer vision (CV) tasks. GCV-Turbo consists of two key components:

A novel hardware architecture that is optimized for the computation kernels in both convolutional neural networks (CNNs) and graph neural networks (GNNs) using the same set of computation resources. This enables efficient execution of both CNN and GNN layers.

A PyTorch-compatible compiler that performs end-to-end optimization for the computation graph of a given GNN-based CV task, including data manipulation between CNN and GNN layers, data layout centric mapping, and sparsity-aware primitive mapping.

The hardware architecture and the compiler work synergistically to support a variety of GNN-based CV tasks. Evaluated on six representative GNN-based CV tasks, GCV-Turbo achieves an average latency reduction of 68.4× and 4.1× compared to state-of-the-art CPU and GPU implementations, respectively. GCV-Turbo also maintains performance comparable to state-of-the-art CNN and GNN accelerators for standalone CNN and GNN models.

Stats

GCV-Turbo achieves an average latency reduction of 68.4× compared to state-of-the-art CPU implementations on six representative GNN-based CV tasks.
GCV-Turbo achieves an average latency reduction of 4.1× compared to state-of-the-art GPU implementations on six representative GNN-based CV tasks.

Quotes

"GCV-Turbo achieves an average latency reduction of 68.4× and 4.1× compared to state-of-the-art CPU and GPU implementations, respectively, on six representative GNN-based CV tasks."
"GCV-Turbo also maintains performance comparable to state-of-the-art CNN and GNN accelerators for standalone CNN and GNN models."

Key Insights Distilled From

GCV-Turbo

by Bingyi Zhang... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07188.pdf

Deeper Inquiries

How can GCV-Turbo's design principles be extended to support other types of hybrid neural network architectures beyond GNN-based computer vision

GCV-Turbo's design principles can be extended to support other types of hybrid neural network architectures by incorporating flexibility in the hardware architecture and compiler optimizations. The key lies in creating a unified hardware design that can efficiently execute various computation primitives from different types of neural networks. By designing a flexible data path and memory organization, the hardware can adapt to the specific requirements of different hybrid architectures. Additionally, the compiler can be enhanced to optimize the computation graph of these hybrid architectures, enabling end-to-end acceleration. By identifying common patterns and computation kernels in different types of neural networks, GCV-Turbo can be tailored to support a broader range of hybrid architectures beyond GNN-based computer vision tasks.

What are the potential challenges in deploying GCV-Turbo in real-world autonomous driving systems that execute a diverse set of computer vision tasks

Deploying GCV-Turbo in real-world autonomous driving systems that execute a diverse set of computer vision tasks may pose several challenges. One potential challenge is the need for real-time inference with low latency, as autonomous driving systems require quick decision-making based on visual inputs. Ensuring that GCV-Turbo can meet the stringent latency requirements of autonomous driving applications is crucial. Another challenge is the integration of GCV-Turbo with existing systems and software frameworks used in autonomous vehicles. Compatibility, scalability, and reliability are essential factors to consider when deploying GCV-Turbo in such complex and safety-critical environments. Furthermore, optimizing power consumption and thermal management to ensure the efficient operation of GCV-Turbo in embedded systems is also a significant challenge in autonomous driving applications.

How can the compiler optimizations in GCV-Turbo be further improved to better exploit the hardware capabilities and reduce the overhead of data layout transformations

To further improve the compiler optimizations in GCV-Turbo and better exploit the hardware capabilities while reducing the overhead of data layout transformations, several strategies can be implemented. Firstly, enhancing the data manipulation layer generation process to efficiently handle the data layout transformations between different types of layers can significantly reduce overhead. By optimizing the data manipulation process and minimizing unnecessary data shuffling, the compiler can streamline the execution flow and improve overall performance. Secondly, incorporating advanced scheduling algorithms and resource allocation strategies in the compiler can help maximize hardware utilization and minimize idle time, leading to more efficient execution of neural network models. Additionally, exploring sparsity-aware optimizations and fine-tuning the mapping of computation primitives based on data sparsity patterns can further enhance the efficiency of GCV-Turbo in handling diverse neural network architectures. By continuously refining and optimizing the compiler optimizations, GCV-Turbo can achieve even higher performance levels and better exploit the capabilities of the underlying hardware architecture.