PipeRTL: Timing-Aware Pipeline Optimization at IR-Level for RTL Generation
Shuo Yin, Fangzhou Liu, Lancheng Zou, Rongliang Fu, Wenqian Zhao, Chen Bai, Tsung-Yi Ho, Yuan Xie, Bei Yu

TL;DR
PipeRTL introduces an IR-level pipeline optimization framework that enhances RTL generation by explicitly modeling register relocation and timing, leading to improved hardware design quality.
Contribution
It presents a novel IR-level pipeline optimization method using a learned timing predictor and min-cost flow formulation, improving downstream hardware implementation.
Findings
Reduces critical-path delay, power, and area in open-source designs.
Provides a stronger starting point for backend retiming.
Improves downstream implementation quality on average.
Abstract
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…
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