Heuristic optimization improves FPGA memory channel resource use
Heuristic-Driven Resource Optimization for Decoupled Memory Channels on FPGAs
Hardware ArchitectureDistributed, Parallel, and Cluster Computing
Summary
Memory controllers on specialized chips like FPGAs need the right size channels to work well without wasting parts. The authors found that making memory channels bigger can actually slow down some programs, a surprising result they call a Performance Inversion Paradox. They created a new method that uses smart rules to choose the best channel size, balancing fast speed with less wasted hardware. Their tests showed this approach can double how efficiently resources are used while keeping top performance.
What this means in practice
- •For fpga hardware engineers: Optimize memory channel sizes in FPGA designs to maximize performance and minimize wasted hardware resources using heuristic-driven analysis.
- •For embedded system developers: Tune OpenCL kernel parameters for efficient memory communication to improve execution speed and reduce hardware overhead in embedded FPGA systems.
Authors
Arnab A. Purkayastha, Shobhit Aggarwal, Jay Tharwani
Abstract
Determining OpenCL channel depths for Decoupled Access-Execute (DAE) architectures in High-Level Synthesis (HLS) is critical for balancing performance and hardware efficiency. Conventional static analysis tools often over-provision these memory channels to ensure performance safety, leading to significant resource stranding. This paper presents an expansion of an LLVM-based static analysis framework, originally proposed for automated DAE generation, by introducing a hardware-aware refinement layer. We identify a counter-intuitive Performance Inversion Paradox linked to baseline memory stalls, where high-stall, grid-based kernels reach peak performance at minimal channel depths and suffer up to a 61% throughput degradation when depths are increased. We propose a Heuristic Refinement Layer that optimizes OpenCL channel depths based on this correlation between baseline stalls and performance saturation. Experimental results demonstrate that our expanded framework successfully identifies the Minimal Peak Performance point across various benchmarks. By avoiding the performance traps of standard over-provisioning, we achieve a 2.52x improvement in Resource Efficiency for an illustrative HotSpot application. This approach maintains peak execution speedup while ensuring the leanest possible hardware profile, outperforming conservative tool-based static predictions.