KForge: Program Synthesis for Diverse AI Hardware Accelerators

• URL: http://arxiv.org/abs/2511.13274v1
• Date: Mon, 17 Nov 2025 11:46:43 GMT
• Title: KForge: Program Synthesis for Diverse AI Hardware Accelerators
• Abstract summary: We present KForge, a platform-agnostic framework built on two collaborative LLM-based agents.<n>A generation agent produces and iteratively refines programs through compilation and correctness feedback, and a performance analysis agent interprets profiling data to guide optimization.<n>This agent-based architecture requires only a single-shot example to target new platforms.
• Score: 5.967639357025406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: GPU kernels are critical for ML performance but difficult to optimize across diverse accelerators. We present KForge, a platform-agnostic framework built on two collaborative LLM-based agents: a generation agent that produces and iteratively refines programs through compilation and correctness feedback, and a performance analysis agent that interprets profiling data to guide optimization. This agent-based architecture requires only a single-shot example to target new platforms. We make three key contributions: (1) introducing an iterative refinement system where the generation agent and performance analysis agent collaborate through functional and optimization passes, interpreting diverse profiling data (from programmatic APIs to GUI-based tools) to generate actionable recommendations that guide program synthesis for arbitrary accelerators; (2) demonstrating that the generation agent effectively leverages cross-platform knowledge transfer, where a reference implementation from one architecture substantially improves generation quality for different hardware targets; and (3) validating the platform-agnostic nature of our approach by demonstrating effective program synthesis across fundamentally different parallel computing platforms: NVIDIA CUDA and Apple Metal.

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