# Agentic-HLS: An agentic reasoning based high-level synthesis system using large language models (AI for EDA workshop 2024)

arXiv: https://arxiv.org/abs/2412.01604 · HTML: https://mlsanity.com/paper/2412.01604/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 1 of 1)

- [DAVE: Deriving Automatically Verilog from English](https://mlsanity.com/paper/2009.01026/) (2020)

## Built on by (most cited first, 4 of 4)

- [Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges](https://mlsanity.com/paper/2511.14478/) (2025)
- [Bench4HLS: End-to-End Evaluation of LLMs in High-Level Synthesis Code Generation](https://mlsanity.com/paper/2601.19941/) (2026)
- [Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?](https://mlsanity.com/paper/2603.25719/) (2026)
- [A3D: Agentic AI flow for autonomous Accelerator Design](https://mlsanity.com/paper/2605.15237/) (2026)

## Abstract (arXiv)

Our aim for the ML Contest for Chip Design with HLS 2024 was to predict the validity, running latency in the form of cycle counts, utilization rate of BRAM (util-BRAM), utilization rate of lookup tables (uti-LUT), utilization rate of flip flops (util-FF), and the utilization rate of digital signal processors (util-DSP). We used Chain-of-thought techniques with large language models to perform classification and regression tasks. Our prediction is that with larger models reasoning was much improved. We release our prompts and propose a HLS benchmarking task for LLMs.