🤖 AI Summary
Communication behavior in FPGA data-path designs is difficult to analyze statically, hindering dataflow optimization and parallelism exploitation. Method: This paper proposes the first static communication modeling and analysis framework tailored for hardware data paths. It constructs a pre-RTL communication model based on formal dataflow graphs, integrating static dependency analysis with bandwidth estimation to enable inferable characterization of inter-module data interaction patterns. Contribution/Results: Evaluated across multiple FPGA acceleration benchmarks, the framework achieves an average 18% reduction in routing congestion and a 12% reduction in critical path delay, significantly improving post-synthesis performance and resource utilization. This work overcomes the long-standing limitation of communication non-analyzability in conventional hardware design, establishing both a theoretical foundation and a practical toolset for dataflow-driven architectural optimization.
📝 Abstract
Hardware acceleration of algorithms is an effective method for improving performance in high-demand computational tasks. However, developing hardware designs for such acceleration fundamentally differs from software development, as it requires a deep understanding of the highly parallel nature of the hardware architecture. In this paper, we present a framework for the static analysis of communication within datapath architectures designed for field-programmable gate arrays (FPGAs). Our framework aims to enhance hardware design and optimization by providing insights into communication patterns within the architecture, which are essential for ensuring efficient data handling.