KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

• URL: http://arxiv.org/abs/2606.02963v1
• Date: Mon, 01 Jun 2026 23:48:55 GMT
• Title: KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
• Abstract summary: Production inference increasingly targets a heterogeneous mix of accelerators.<n>This creates a systems challenge: each pipeline now requires high-performance kernels across a growing set of hardware backends and programming models.<n>We present KForge, a cross-platform framework built around an iterative refinement loop driven by two collaborating LLM-based agents.
• Score: 5.703659894138959
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Production inference increasingly targets a heterogeneous mix of accelerators. Agentic pipelines interleave reasoning, tool calls, and multi-agent coordination, each with distinct compute and memory profiles. For optimal efficiency, each stage should run on the accelerator best suited to it. This creates a systems challenge: each pipeline now requires high-performance kernels across a growing set of hardware backends and programming models. Writing these kernels by hand is time-consuming, demands deep low-level expertise, and does not scale as kernel complexity grows. Recently, Large Language Models (LLMs) have been leveraged for automatic kernel generation, but challenges in low-level code generation and cross-backend generalization persist. We present KForge, a cross-platform framework built around an iterative refinement loop driven by two collaborating LLM-based agents: a generation agent that produces and progressively refines kernels using compilation and correctness feedback, and a performance-analysis agent that interprets profiling data, from programmatic APIs to GUI-based tools, and emits recommendations that steer the next round of synthesis. The loop alternates between functional passes, which drive a candidate to correctness, and optimization passes, which close the performance gap to hand-tuned baselines. We evaluate KForge on two backends with very different baseline reference availability. On NVIDIA B200, KForge achieves a 2.12$\%$ improvement in end-to-end throughput compared to TensorRT-LLM on the gpt-oss-20b inference speed benchmark. On Intel Arc B580, KForge generates Triton kernels achieving a 5.13$\times$ geometric mean speedup over the faster of PyTorch eager and torch.compile on 37 GEMM + tail-ops workloads from KernelBench Level 2, primarily via operator fusion and mixed-precision execution.

Related papers

• MaxKernel: Agentic Kernel Generation for TPUs [0.1719024873384565]
  MaxKernel is a multi-agent system that implements three distinct paradigms for TPU kernel development.<n>All three paradigms leverage a shared pool of specialized sub-agents to handle planning, implementation, self-ging, testing, and hardware profiling.<n>We demonstrate that Max Kernel consistently generates highly optimized implementations, matching expert hand-tuned baselines.
  arXiv  Detail & Related papers  (2026-09-03T22:22:37Z)
• Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding [51.48425605758328]
  We present Faster Flash Decoding (FFD), a novel hardware-algorithm co-design framework designed to break the memory wall in long-context decoding.<n>FFD integrates the selector and computer into a fully fused kernel, replacing external metadata indices with content-aware scanning.<n>It achieves up to 11.6x kernel-level speedup and scaling to 256K context length, with 2.37x end-to-end throughput improvement.
  arXiv  Detail & Related papers  (2026-08-31T15:13:20Z)
• Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling [61.28599393707943]
  Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generation timesteps.<n>We propose a task-shared core scorer and progress-conditioned generation expansions, optimized through generation decomposition and cross-task core alignment.<n> Experiments on two representative UMM architectures demonstrate consistent quality--efficiency improvements across both tasks, retaining 98.03% of dense understanding performance with a 1.93times$ end-to-end inference speedup.
  arXiv  Detail & Related papers  (2026-08-29T14:27:55Z)
• Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization [37.645358922637065]
  KernelPro is a closed-loop multi-agent system that automatically generates, profiles, and iteratively optimize kernel code.<n>On Kernel, KernelPro achieves mean speedups of 2.42x/4.69x/5.30x on Levels 1/2/3, establishing state-of-the-art performance across all difficulty levels.
  arXiv  Detail & Related papers  (2026-06-24T23:28:09Z)
• Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU [38.81440160993858]
  Porting deep learning algorithms to new hardware accelerators requires developers to apply the same low-level optimizations to every Triton kernel in their code-base.<n>We present Xe-Forge, a multi-stage LLM-powered pipeline that automates this process for Intel GPU.<n>A curated knowledge base encodes Intel GPU constraints that are absent from LLM training data, keeping the model within architecturally valid bounds.
  arXiv  Detail & Related papers  (2026-04-16T20:21:01Z)
• AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization [84.25316984309725]
  Integration of dynamic, sparse structures with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs)<n>Despite minimal increases in computational load, the computation latency often skyrockets, leading to decoding speeds slowing by over 2.5 times.<n>AdaFuse is a framework built on a tight co-design between the algorithm and the underlying hardware system to enable efficient dynamic adapter execution.
  arXiv  Detail & Related papers  (2026-03-12T12:46:42Z)
• StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning [26.264303471292845]
  We propose StitchCUDA, a multi-agent framework for end-to-end GPU program generation.<n> Experiments show that StitchCUDA achieves nearly 100% success rate on end-to-end programming tasks, with 1.72x better speedup than the multi-agent baseline.
  arXiv  Detail & Related papers  (2026-03-03T06:04:49Z)
• AscendCraft: Automatic Ascend NPU Kernel Generation via DSL-Guided Transcompilation [8.878393510726008]
  We present AscendCraft, a DSL-guided approach for automatic AscendC kernel generation.<n>AscendingCraft achieves 98.1% compilation success and 90.4% functional correctness.<n>We also demonstrate that DSL-guided transcompilation can enable LLMs to generate both correct and competitive NPU kernels.
  arXiv  Detail & Related papers  (2026-01-30T09:34:59Z)
• AKG kernel Agent: A Multi-Agent Framework for Cross-Platform Kernel Synthesis [13.239454996851771]
  Modern AI models demand high-performance computation kernels.<n>Akg kernel agent (AI-driven Kernel Generator) is designed to support multiple domain-specific languages.<n>System's modular design allows rapid integration of backend DSLs and hardware targets.<n>System achieves an average speedup of 1.46$times over PyTorch Eager baselines.
  arXiv  Detail & Related papers  (2025-12-29T12:42:05Z)
• An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks [0.5737287537823071]
  Rotation equivariant graph neural networks yield state of the art performance on spatial deep learning tasks.<n>Key to these models is the Clebsch-Gordon (CG) tensor product, a kernel that contracts two dense feature vectors with a highly-structured sparse tensor to produce a dense output vector.<n>We introduce a GPU sparse kernel generator for the CG tensor product that provides significant speedups over the best existing open and closed-source implementations.
  arXiv  Detail & Related papers  (2025-01-23T08:20:47Z)
• Kernel Looping: Eliminating Synchronization Boundaries for Peak Inference Performance [2.747566636546902]
  Kernel looping exploits the unique layer-level fusion possible in modern dataflow architectures with the repeated layer structure found in language models. We evaluate kernel looping on the SambaNova SN40L Reconfigurable Dataflow Unit (RDU), a commercial dataflow accelerator for AI.
  arXiv  Detail & Related papers  (2024-10-31T06:32:47Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference [23.633481089469836]
  Auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance.<n>We propose a novel parallel prompt decoding that requires only $0.0002$% trainable parameters, enabling efficient training on a single A100-40GB GPU in just 16 hours.<n>Our approach demonstrates up to 2.49$times$ speedup and maintains a minimal memory overhead of just $0.0004$%.
  arXiv  Detail & Related papers  (2024-05-28T22:19:30Z)
• Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
  This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels. We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
  arXiv  Detail & Related papers  (2023-04-25T05:04:44Z)
• PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives [55.79741270235602]
  We develop a hybrid solution to the development of deep learning kernels. We use the advanced polyhedral technology to automatically tune the outer loops for performance.
  arXiv  Detail & Related papers  (2020-02-06T08:02:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.