MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Acceleration

2025-02-09

AI Summary

Deploying deep neural networks (DNNs) on resource-constrained hardware such as FPGAs poses a fundamental challenge in simultaneously optimizing performance, accuracy, and hardware resource utilization (e.g., DSPs and LUTs), while heavily relying on manual expertise. Method: This paper introduces the first unified, fully automated framework that integrates high-level synthesis (HLS) metaprogramming with programmable DNN optimization. It jointly models compiler optimizations, hardware mapping, and model compression, enabling customizable transformations and Bayesian-driven design-space exploration for end-to-end, cross-stage co-optimization. Contribution/Results: Experimental evaluation demonstrates that, without sacrificing original model accuracy, the framework reduces DSP and LUT usage by up to 92% and 89%, respectively, and accelerates optimization efficiency by 15.6× over exhaustive grid search—significantly diminishing reliance on domain expertise and manual tuning.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper presents a unified framework for codifying and automating optimization strategies to efficiently deploy deep neural networks (DNNs) on resource-constrained hardware, such as FPGAs, while maintaining high performance, accuracy, and resource efficiency. Deploying DNNs on such platforms involves addressing the significant challenge of balancing performance, resource usage (e.g., DSPs and LUTs), and inference accuracy, which often requires extensive manual effort and domain expertise. Our novel approach addresses two key issues: cross-stage co-optimization and optimization search. By seamlessly integrating programmatic DNN optimization techniques with high-level synthesis (HLS)-based metaprogramming and leveraging advanced design space exploration (DSE) strategies like Bayesian optimization, the framework automates both top-down and bottom-up design flows, reducing the need for manual intervention and domain expertise. The proposed framework introduces customizable optimization, transformation, and control blocks to enhance DNN accelerator performance and resource efficiency. Experimental results demonstrate up to a 92% DSP and 89% LUT usage reduction for select networks, while preserving accuracy, along with a 15.6-fold reduction in optimization time compared to grid search. These results underscore the novelty and potential of the proposed framework for automated, resource-efficient DNN accelerator designs.

Problem

• Automates DNN optimization for resource-constrained hardware
• Enhances performance, accuracy, and resource efficiency on FPGAs
• Reduces manual effort and optimization time significantly

Innovation

• Automates DNN optimization for FPGAs
• Integrates HLS with Bayesian DSE
• Reduces manual intervention and expertise