From Pragmas to Partners: A Symbiotic Evolution of Agentic High-Level Synthesis

TL;DR

This paper argues that high-level synthesis remains crucial in AI-driven hardware design, emphasizing its role in enabling agentic optimization and proposing a taxonomy for its evolution alongside AI agents.

Contribution

It explains HLS as a practical abstraction layer, identifies current limitations, and proposes a taxonomy for its evolution with AI agents.

Findings

HLS is a key abstraction for agentic hardware design.

Current HLS tools have limitations in feedback, interfaces, and debugging.

A taxonomy for the evolution of agentic HLS is proposed.

Abstract

The rise of large language models has sparked interest in AI-driven hardware design, raising the question: does high-level synthesis (HLS) still matter in the agentic era? We argue that HLS remains essential. While we expect mature agentic hardware systems to leverage both HLS and RTL, this paper focuses on HLS and its role in enabling agentic optimization. HLS offers faster iteration cycles, portability, and design permutability that make it a natural layer for agentic optimization. This position paper makes three contributions. First, we explain why HLS serves as a practical abstraction layer and a golden reference for agentic hardware design. Second, we identify key limitations of current HLS tools, namely inadequate performance feedback, rigid interfaces, and limited debuggability that agents are uniquely positioned to address. Third, we propose a taxonomy for the symbiotic evolution of agentic HLS, clarifying how responsibility shifts from human designers to AI agents as systems advance from copilots to autonomous design partners.