insight - Computer Science - # ViT Acceleration Algorithm

Accelerating ViT Inference on FPGA through Static and Dynamic Pruning

Q: How does the proposed simultaneous pruning approach address accuracy drop?

The proposed simultaneous pruning approach addresses accuracy drop by utilizing a knowledge distillation technique commonly used to transfer knowledge from a larger teacher model to a smaller student model. By distilling the knowledge from the original, unpruned model into the pruned model during training, it helps in recovering accuracy lost due to pruning. This technique ensures that even with reduced parameters and tokens, the pruned ViT model can still maintain a high level of accuracy comparable to the original unpruned model.

Q: What are the implications of integrating block-wise weight pruning and token dropping in ViT acceleration?

Integrating block-wise weight pruning and token dropping in ViT acceleration has several implications: Reduced Computational Complexity: Weight pruning reduces redundant parameters in MSA matrices, leading to decreased computational demands. Token dropping further reduces computation by removing unnecessary or less important tokens. Model Size Reduction: Both techniques combined result in significant reduction in model size as well as associated computational complexity. Improved Efficiency: The integration allows for more efficient hardware execution on FPGA by optimizing irregular computation patterns caused by both types of pruning methods. Maintained Accuracy: Despite reducing complexity and size through these methods, maintaining accuracy is crucial, which is achieved through simultaneous training algorithms that recover any loss due to pruning.

Q: How does FPGA outperform CPU/GPU platforms in handling irregular computation patterns?

FPGA outperforms CPU/GPU platforms in handling irregular computation patterns primarily due to its customizable nature and parallel processing capabilities: Customized Data Path: FPGAs allow for customized data path design tailored specifically for handling irregular computations resulting from complex models like ViTs after applying various forms of pruning. Parallelism: FPGAs offer multi-level parallelism strategies that efficiently deal with irregular access patterns caused by combining weight and token-based pruning approaches. Efficient Resource Utilization: With proper optimization and resource allocation on FPGA architecture, load balancing across different columns for sparse matrix multiplication becomes more effective compared to traditional CPU/GPU setups. On-the-Fly Processing: FPGAs can efficiently handle dynamic operations such as token shuffling required during inference when implementing dynamic token dropping algorithms. Overall, FPGA's flexibility, parallel processing capabilities, and efficient resource utilization make it better suited for accelerating models like Vision Transformers with complex architectures involving multiple layers of attention mechanisms while incorporating advanced optimization techniques like static weight pruning and dynamic token dropping simultaneously.

Core Concepts

Proposing a comprehensive algorithm-hardware codesign for accelerating ViT on FPGA through simultaneous pruning.

Abstract

The content discusses the challenges of accelerating Vision Transformers (ViTs) on FPGA through static and dynamic pruning. It introduces weight and token pruning methods to reduce computational complexity, proposing a novel algorithm-hardware codesign approach. The article details the algorithm design combining static weight pruning and dynamic token pruning, along with hardware design employing multi-level parallelism for efficient execution. Evaluation results show significant latency reduction and model compression ratios compared to CPU, GPU implementations.
Structure:

Introduction to ViTs and their computational challenges.
Weight and token pruning methods for reducing complexity.
Proposed algorithm-hardware codesign approach.
Implementation details on FPGA platform.
Evaluation metrics and results.

Stats

"The proposed algorithm can reduce computation complexity by up to 3.4× with ≈3% accuracy drop."
"Our codesign on FPGA achieves average latency reduction of 12.8×, 3.2×, 0.7 −2.1× respectively."

Quotes

"Weight pruning reduces the model size and associated computational demands."
"Token pruning further dynamically reduces the computation based on the input."

Key Insights Distilled From

Accelerating ViT Inference on FPGA through Static and Dynamic Pruning

by Dhruv Parikh... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14047.pdf

Accelerating ViT Inference on FPGA through Static and Dynamic Pruning

Deeper Inquiries

How does the proposed simultaneous pruning approach address accuracy drop?

The proposed simultaneous pruning approach addresses accuracy drop by utilizing a knowledge distillation technique commonly used to transfer knowledge from a larger teacher model to a smaller student model. By distilling the knowledge from the original, unpruned model into the pruned model during training, it helps in recovering accuracy lost due to pruning. This technique ensures that even with reduced parameters and tokens, the pruned ViT model can still maintain a high level of accuracy comparable to the original unpruned model.

What are the implications of integrating block-wise weight pruning and token dropping in ViT acceleration?

Integrating block-wise weight pruning and token dropping in ViT acceleration has several implications:

Reduced Computational Complexity: Weight pruning reduces redundant parameters in MSA matrices, leading to decreased computational demands. Token dropping further reduces computation by removing unnecessary or less important tokens.
Model Size Reduction: Both techniques combined result in significant reduction in model size as well as associated computational complexity.
Improved Efficiency: The integration allows for more efficient hardware execution on FPGA by optimizing irregular computation patterns caused by both types of pruning methods.
Maintained Accuracy: Despite reducing complexity and size through these methods, maintaining accuracy is crucial, which is achieved through simultaneous training algorithms that recover any loss due to pruning.

How does FPGA outperform CPU/GPU platforms in handling irregular computation patterns?

FPGA outperforms CPU/GPU platforms in handling irregular computation patterns primarily due to its customizable nature and parallel processing capabilities:

Customized Data Path: FPGAs allow for customized data path design tailored specifically for handling irregular computations resulting from complex models like ViTs after applying various forms of pruning.
Parallelism: FPGAs offer multi-level parallelism strategies that efficiently deal with irregular access patterns caused by combining weight and token-based pruning approaches.
Efficient Resource Utilization: With proper optimization and resource allocation on FPGA architecture, load balancing across different columns for sparse matrix multiplication becomes more effective compared to traditional CPU/GPU setups.
On-the-Fly Processing: FPGAs can efficiently handle dynamic operations such as token shuffling required during inference when implementing dynamic token dropping algorithms.

Overall, FPGA's flexibility, parallel processing capabilities, and efficient resource utilization make it better suited for accelerating models like Vision Transformers with complex architectures involving multiple layers of attention mechanisms while incorporating advanced optimization techniques like static weight pruning and dynamic token dropping simultaneously.

Accelerating ViT Inference on FPGA through Static and Dynamic Pruning