Holistic Optimization Framework for FPGA Accelerators

2025-01-16

AI Summary

Field-programmable gate array (FPGA) accelerators face a fundamental trade-off among performance, energy efficiency, and quality-of-results (QoR) under stringent resource constraints. Method: This paper proposes the first end-to-end co-optimization framework that unifies task fusion, tiling, loop reordering, compute-communication overlap, and concurrent execution—embedding multi-dimensional hardware optimization strategies into a single nonlinear design space. The approach integrates nonlinear optimization, high-level synthesis (HLS) directive-aware scheduling, source-to-source compilation, dataflow modeling, and parallel task mapping to enable fully automated, resource-constrained, global QoR-optimal bitstream generation. Contribution/Results: Evaluated on multiple benchmarks, the framework achieves 23–41% higher QoR compared to state-of-the-art design-space exploration (DSE) tools, while significantly improving energy efficiency and throughput.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Customized accelerators have transformed modern computing by enhancing energy efficiency and performance through specialization. Field Programmable Gate Arrays play a pivotal role in this domain due to their flexibility and high-performance potential. High-Level Synthesis and source-to-source compilers simplify hardware design by converting high-level code into hardware descriptions enriched with directives. However, achieving high Quality of Results in FPGA designs remains challenging, requiring complex transformations, strategic directive use, and efficient data management. While existing approaches like Design Space Exploration (DSE) and source-to-source compilers have made strides in improving performance, they often address isolated aspects of the design process. This paper introduces Prometheus, a holistic framework that integrates task fusion, tiling, loop permutation, computation-communication overlap, and concurrent task execution into a unified design space. Leveraging Non-Linear Problem methodologies, Prometheus explores this space to find solutions under resource constraints, enabling bitstream generation.

Problem

• FPGA optimization
• resource limitation
• comprehensive performance enhancement

Innovation

• FPGA optimization
• comprehensive toolset
• advanced optimization methods