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High-Level Synthesis of Efficient Pipelines with Visibility Control

Jungin Rhee, Minseong Jang, Jaewoo Kim, Jeehoon Kang 2026-07-23
Design Space Exploration × Agentic Architecture

The problem is that existing high-level synthesis (HLS) tools either lack fine-grained control over pipeline structure and hazard resolution or sacrifice sequential semantics to provide it. The method introduces an HLS tool built on visibility control, a novel programming abstraction that unifies hazard resolution strategies including stalling, bypassing, speculation, deferred commit, and register renaming within a sequential programming model. Experimental evidence shows that on in-order RISC-V cores, histograms, and an AES accelerator, compiled pipelines outperform HLS tools with sequential semantics and achieve power, performance, and area (PPA) comparable to hand-written RTL. This matters because it enables rapid design-space exploration for efficient pipelines without requiring RTL or concurrent programming models.

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A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors

Philip John, Eloghosa Ikponmwoba, Pinaki Pal, Opeoluwa Owoyele 2026-07-23
Design Space Exploration × Agentic Architecture Network

The problem is that existing methods for determining cluster boundaries in reactor network models rely on manual heuristics or distance-based metrics, which are not optimized for target metrics like lean blowout (LBO) prediction accuracy. The method introduces a reinforcement learning (RL) framework that uses a multi-stage clustering-classification strategy, where an actor-critic RL agent merges initial micro-clusters into optimal reactor zones in a goal-oriented manner. Experimental evidence from a validation study using a Jet-A mechanism (119 species, 841 reactions) shows that the RL framework improves predictive fidelity over k-means clustering and captures correct LBO trends while achieving substantial speedups relative to high-fidelity computational models. This matters because the RL-driven approach provides a computationally efficient reduced-order modeling technique that can complement high-fidelity simulations for rapid design-space exploration in gas turbine combustors.

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