D\'ej\`a Vu Packing: Optimizing FPGA Logic Clustering Runtime via Pattern Memoization

TL;DR

This paper introduces D'ejà Vu packing, a method that significantly accelerates FPGA logic clustering by memoizing recurring pattern legality checks, reducing overall CAD flow runtime.

Contribution

It proposes a novel packing signature tree data structure to identify and reuse packing legality outcomes, improving FPGA clustering efficiency.

Findings

Speeds up packing by up to 29.3x on average.

Reduces end-to-end VPR runtime by up to 5.3x.

Maintains quality of results despite runtime improvements.

Abstract

Implementing a digital circuit on an FPGA fabric requires clustering technology-mapped netlist primitives into coarser-granularity blocks that can be directly mapped to the physical resources available on the FPGA. As the architecture of FPGA logic blocks (LBs) has grown in complexity, with sophisticated logic elements (LEs) and highly irregular local interconnect, this packing problem has become more challenging. To ensure the feasibility of intracluster routing, the computer-aided design (CAD) tools must solve a costly multi-source multi-sink routing problem for each candidate cluster. In this paper, we first show that such packing legality checks consume a significant portion of the CAD flow runtime for LB architectures with complex LEs and local routing structures resembling modern commercial FPGAs. We demonstrate that the packing stage constitutes 58% and 94% of the entire Versatile Place and Route (VPR) flow runtime on average when mapping a wide variety of benchmarks to the AMD 7-series-like and Altera Stratix-10-like VTR architecture captures, respectively. By analyzing the packing algorithm behavior, we observe that a significant fraction of the attempted packed clusters are repetitions of a much smaller number of packing patterns, and therefore many of the packing legality checks are redundant and could be skipped. To this end, we introduce our D\'ej\`a Vu packing approach, which leverages a novel packing signature tree data structure that enables efficient identification of recurring packing patterns and memoization of their legality check outcomes. Our approach speeds up the packing by up to 13.4x and 29.3x, with an average of 3.7x and 6.9x, across the evaluated benchmarks on the 7-series and Stratix 10 architectures. These packing runtime gains result in a significant 1.6x and 5.3x average reduction in end-to-end VPR runtime, while maintaining quality of results.