ARMay 3

PipeRTL: Timing-Aware Pipeline Optimization at IR-Level for RTL Generation

arXiv:2605.0183647.51 citationsHas Code
Predicted impact top 33% in AR · last 90 daysOriginality Highly original
AI Analysis

For hardware designers and compiler developers, PipeRTL addresses the problem of limited pipeline optimization opportunities in current RTL flows by moving optimization earlier to the IR level, enabling global improvements.

PipeRTL introduces an IR-level pipeline optimization framework for hardware compilers that uses a learned timing predictor and min-cost flow formulation to improve downstream implementation quality, reducing critical-path delay, power, and area across evaluated benchmarks.

Modern hardware compilers increasingly rely on rich intermediate representations (IRs) to preserve optimization-relevant semantics before generating RTL code. However, one important optimization is still largely deferred to backend tools: pipeline optimization. In common RTL flows, registers are inserted by frontend heuristics or hardware designers and later adjusted by backend retiming after the design has been lowered to a much lower-level netlist representation. At that point, much of the operator-level structure originally exposed by the compiler IR has already been weakened or lost, limiting opportunities for global, compiler-level pipeline optimization. This paper presents PipeRTL, an IR-level pipeline optimization framework for hardware compilers, instantiated in CIRCT. PipeRTL makes the legality of register relocation explicit in the IR, uses a learned timing predictor to approximate downstream delay behavior, and formulates timing-aware register relocation as a global min-cost flow problem under timing constraints. Evaluation on open-source designs under a commercial backend synthesis flow shows that PipeRTL improves downstream implementation quality on average, reducing critical-path delay, power, and area across the evaluated benchmarks, while also providing a stronger starting point for backend retiming. These results indicate that exposing pipeline optimization as an explicit compiler pass can deliver backend-meaningful gains by improving the sequential structure presented to later stages and the resulting downstream implementation quality.

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