An FPGA-Based Reconfigurable Accelerator for Efficient Vision Transformer with Convolution-Transformer Hybrid Architecture

Concetti Chiave

This paper proposes an FPGA-based accelerator design to efficiently support the Convolution-Transformer hybrid architecture of the state-of-the-art efficient Vision Transformer, EfficientViT, by leveraging a reconfigurable architecture and a novel time-multiplexed and pipelined dataflow.

Sintesi

The paper presents an FPGA-based accelerator design for the efficient Vision Transformer (ViT) model called EfficientViT. EfficientViT features a Convolution-Transformer hybrid architecture, comprising lightweight convolutions (MBConvs) and a lightweight Multi-Scale Attention (MSA) module.

The key contributions are:

1. Reconfigurable Architecture Design:

   • A reconfigurable processing element (RPE) architecture is designed to efficiently support various operation types in EfficientViT, including lightweight convolutions and lightweight attention.
   • The RPE can operate in either DW mode for depthwise convolutions or PW mode for pointwise convolutions and generic convolutions.

2. Time-Multiplexed and Pipelined Dataflow:

   • A novel time-multiplexed and pipelined (TMP) dataflow is proposed to fuse computations among adjacent lightweight convolutions and computations within the lightweight attention module.
   • This dramatically boosts computing resource utilization while easing bandwidth requirements.

3. Accelerator Design and Evaluation:

   • The proposed accelerator incorporates both the RPE engine and a MAT (multipliers and adder-trees) engine to efficiently execute the various operations in EfficientViT.
   • Implemented on the Xilinx ZCU102 FPGA, the accelerator achieves up to 780.2 GOPS in throughput and 105.1 GOPS/W in energy efficiency, significantly outperforming prior works.

Statistiche

The proposed accelerator achieves up to 780.2 GOPS in throughput and 105.1 GOPS/W in energy efficiency when implemented on the Xilinx ZCU102 FPGA at 200MHz.

Citazioni

"To fully unleash its hardware benefit potential, it is highly desired to develop a dedicated accelerator for EffieicientViT, which, however, poses challenges due to its dynamic workloads and high-intensity memory access demands."
"Particularly, EfficientViT involves various operation types, including lightweight convolutions (i.e., MBConvs) with different kernel sizes, strides, and feature dimensions, as well as the lightweight attention (i.e., MSA), which exhibits distinct computational patterns compared to the vanilla self-attention in standard ViTs."

Approfondimenti chiave tratti da

An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT

by Haikuo Shao,... alle arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20230.pdf

Domande più approfondite

How can the proposed accelerator design be extended to support other efficient ViT models beyond EfficientViT

The proposed accelerator design can be extended to support other efficient ViT models beyond EfficientViT by incorporating model-specific optimizations and adaptations. Since different efficient ViT models may have varying architectural characteristics and computational requirements, the reconfigurable architecture of the accelerator can be further customized to accommodate these differences. By analyzing the unique features of each model, such as the types of convolutions used, attention mechanisms, and activation functions, the accelerator can be tailored to efficiently handle the specific operations required by each model. Additionally, the time-multiplexed dataflow approach can be adjusted to suit the computational patterns and fusion requirements of different ViT architectures, ensuring optimal performance across a range of models.

What are the potential limitations or trade-offs of the reconfigurable architecture and time-multiplexed dataflow approach, and how can they be further optimized

While the reconfigurable architecture and time-multiplexed dataflow approach offer significant advantages in enhancing hardware utilization and reducing off-chip data access costs, there are potential limitations and trade-offs that need to be considered. One limitation could be the complexity of managing the reconfigurability of the architecture, which may introduce overhead in terms of configuration time and resource allocation. To address this, further optimization techniques such as automated configuration management and resource allocation algorithms can be implemented to streamline the reconfiguration process and minimize overhead. Additionally, the time-multiplexed dataflow approach may introduce latency due to the pipelined nature of computations. To mitigate this, fine-tuning the pipeline stages and optimizing the dataflow scheduling can help reduce latency and improve overall throughput without compromising hardware efficiency.

What are the implications of the accelerator's performance on the real-world deployment of efficient ViT models in resource-constrained edge devices

The performance of the accelerator has significant implications for the real-world deployment of efficient ViT models in resource-constrained edge devices. By achieving high throughput and energy efficiency, the accelerator enables efficient inference of ViT models on edge devices with limited computational resources. This advancement opens up opportunities for deploying state-of-the-art vision transformers in applications where real-time processing and low power consumption are critical, such as edge AI, IoT devices, and autonomous systems. The ability to efficiently run efficient ViT models on edge devices can lead to improved accuracy and performance in vision-related tasks while maintaining low power consumption, making it feasible to deploy advanced computer vision capabilities in a wide range of edge computing scenarios.