Accelerating Boolean Constraint Propagation for Efficient SAT-Solving on FPGA-Based Systems

Основні поняття

A novel hardware-accelerated SAT solver architecture that outperforms state-of-the-art solutions by efficiently mapping and runtime-managing clauses across a processor and connected FPGA.

Анотація

The paper presents a hardware-accelerated SAT solver that targets processor/FPGA SoCs. The key contributions are:

A methodology to efficiently map and runtime-manage clauses across a processor and connected FPGA, making effective use of FPGA resources and avoiding performance pitfalls.
An open-source prototype system deployed on a Xilinx Zynq chip, demonstrating the hardware architecture and its impact on the proposed strategy.
Evaluation against state-of-the-art solutions, showing speedups of 1.7x and 1.1x on BCP for two representative benchmarks, and up to 6x improvement over a software-only implementation.

The authors identify that the performance of their approach is constrained by the clause partitioning strategy, which determines the required runtime swapping of partitions. They suggest that future work should focus on developing an effective partitioning algorithm to further improve the overall system performance.

Статистика

Our solution achieves a clock frequency of 106.66 MHz, utilizing 647 LUTRAM of on-chip memory, 13151 LUTs, and 11059 FFs on a Xilinx Zynq chip.
Compared to prior state-of-the-art, we achieve 1.7x and 1.1x speed up on BCP for 2 representative benchmarks.
We achieve up to 6x total speedup over software-only implementation.

Цитати

"We present a hardware-accelerated SAT solver targeting processor/Field Programmable Gate Arrays (FPGA) SoCs."
"Our solution accelerates the most expensive subroutine of the Davis-Putnam-Logemann-Loveland (DPLL) algorithm, Boolean Constraint Propagation (BCP) through fine-grained FPGA parallelism."
"Compared to prior state-of-the-art, we achieve 1.7x and 1.1x speed up on BCP for 2 representative benchmarks and up to 6x total speedup over software-only implementation."

Ключові висновки, отримані з

Accelerating Boolean Constraint Propagation for Efficient SAT-Solving on FPGAs

by Hariprasadh ... о arxiv.org 04-16-2024

https://arxiv.org/pdf/2401.07429.pdf

Глибші Запити

How can the clause partitioning strategy be further optimized to minimize the required runtime swapping of partitions and improve overall system performance?

To optimize the clause partitioning strategy for the hardware-accelerated SAT solver, several approaches can be considered:

Variable-Centric Partitioning: Instead of randomly partitioning the clauses, a variable-centric approach can be adopted. This strategy involves grouping together clauses that share common variables. By doing so, the number of variables that need to be swapped in and out during runtime can be minimized, reducing the overhead of partition swapping.

Dynamic Partitioning: Implementing a dynamic partitioning scheme that adapts to the characteristics of the SAT instance being solved can lead to better performance. This approach involves analyzing the structure of the formula and dynamically adjusting the partition sizes and configurations based on the current state of the solver.

Overlap Partitioning: Introducing overlap between partitions can help in reducing the need for frequent swapping. By allowing clauses to be present in multiple partitions, the system can avoid unnecessary data transfers and improve the efficiency of clause processing.

Load-Balanced Partitioning: Ensuring that partitions are evenly distributed in terms of workload can prevent bottlenecks and maximize the utilization of the available resources. Load-balanced partitioning can help in achieving better parallelism and overall system performance.

By implementing these optimized partitioning strategies, the hardware-accelerated SAT solver can minimize runtime swapping, enhance data locality, and improve the efficiency of clause processing, leading to better overall system performance.

How could the proposed hardware-accelerated SAT solver be integrated into larger systems or applications to leverage its performance benefits?

The hardware-accelerated SAT solver can be integrated into larger systems or applications in the following ways to leverage its performance benefits:

Embedded Systems: Incorporating the SAT solver into embedded systems can enhance the efficiency of decision-making processes in real-time applications. For example, in autonomous vehicles, the solver can be used for route planning and obstacle avoidance.

Cloud Computing: Deploying the SAT solver in cloud computing environments can provide scalable and high-performance SAT solving capabilities to users. This can be particularly useful for complex optimization problems in various domains.

Cyber-Physical Systems: Integrating the solver into cyber-physical systems can improve the system's ability to make intelligent decisions based on real-time data. This can be beneficial in applications such as smart grids, healthcare systems, and industrial automation.

High-Performance Computing: Utilizing the solver in high-performance computing clusters can accelerate the resolution of large-scale SAT instances, enabling faster problem-solving in scientific research, data analysis, and computational biology.

By integrating the hardware-accelerated SAT solver into diverse systems and applications, organizations can leverage its speed and efficiency to tackle complex problems more effectively, leading to improved performance and productivity.