EQ-ViT: Algorithm-Hardware Co-Design for End-to-End Acceleration of Real-Time Vision Transformer Inference on Versal ACAP Architecture
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces EQ-ViT, an acceleration framework enabling real-time Vision Transformer (ViT) deployment. EQ-ViT achieves significant speedups and accuracy improvements on AMD Versal ACAP, overcoming latency challenges in computer vision applications.
Area Of Science
- Computer Vision
- Hardware Acceleration
- Machine Learning
Background
- Vision Transformers (ViTs) show promise in computer vision but face deployment challenges for real-time (< 1 ms) applications.
- Existing platforms (CPUs, GPUs, FPGAs) struggle with deterministic low-latency requirements, even with model quantization.
- Pruning and sparsity techniques reduce model size but often lead to accuracy loss.
Purpose Of The Study
- To propose EQ-ViT, an end-to-end acceleration framework for real-time ViT deployment.
- To co-design algorithms and hardware architectures for efficient ViT acceleration on AMD Versal ACAP.
- To overcome the accuracy-latency trade-off in current ViT acceleration methods.
Main Methods
- In-depth kernel-level performance profiling to identify bottlenecks in existing acceleration solutions.
- Development of a spatial and heterogeneous EQ-ViT architecture leveraging ACAP's FPGA and AI Engine (AIE) resources.
- Implementation of a quantization-aware training strategy (EQ-ViT algorithm) for 8-bit weight and activation quantization, including nonlinear functions.
- Design of an automation framework to deploy EQ-ViT for various ViT applications on AMD Versal ACAP.
Main Results
- Achieved a 2.4% accuracy improvement with EQ-ViT.
- Obtained average speedups of 315.0x over vCPUs, 3.39x-59.5x over GPUs, and 3.38x-13.1x over FPGAs.
- Demonstrated significant energy efficiency gains, ranging from 12.82x to 62.2x compared to various computing solutions.
Conclusions
- EQ-ViT effectively enables real-time Vision Transformer acceleration on AMD Versal ACAP.
- The proposed framework achieves superior performance and energy efficiency without compromising accuracy.
- EQ-ViT offers a viable solution for deploying demanding computer vision tasks in real-time scenarios.
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