AgenticCANN: Automated Ascend C Operator Generation via Knowledge-Augmented Agentic Evolution

• URL: http://arxiv.org/abs/2607.26661v2
• Date: Mon, 03 Aug 2026 13:17:55 GMT
• Title: AgenticCANN: Automated Ascend C Operator Generation via Knowledge-Augmented Agentic Evolution
• Abstract summary: AgenticCANN is a knowledge-augmented agentic evolution framework specifically tailored for automated Ascend C operator synthesis in low-corpus NPU environments.<n>Our method achieves 90 to 100 percent feasibility on elementwise and normalization operators, 56% on fusion operators, and up to 6.65$times$ speedup on 1B Pangu model inference kernels.
• Score: 16.709206818546317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ascend C operator optimization is critical for NPU (Neural Processing Unit) inference performance but requires deep hardware expertise. While large language models (LLMs) have shown promise in automated CUDA kernel generation, the fundamentally different programming model of Ascend C introduces unique challenges that remain unexplored. In this paper, we propose AgenticCANN, a knowledge-augmented agentic evolution framework specifically tailored for automated Ascend C operator synthesis in low-corpus NPU environments. To overcome the severe platform knowledge deficit on unfamiliar hardware, AgenticCANN incorporates a knowledge-orchestrated generation system that delivers structured, multi-level domain insights across the development lifecycle to resolve the upstream feasibility bottleneck. Building on this foundation, it features a stage-adaptive agentic evolution strategy that dynamically aligns LLM interaction modes with specific generation and evolution phases, balancing high-exploration candidate discovery with high-convergence performance tuning. Extensive experiments on Huawei Ascend 910B across six operators spanning five pattern categories demonstrate that our method achieves 90 to 100 percent feasibility on elementwise and normalization operators, 56% on fusion operators, and up to 6.65$\times$ speedup on 1B Pangu model inference kernels. Further analysis reveals that knowledge injection monotonically improves feasibility from 57% to 86% on elementwise operators, demonstrating its general rather than operator-specific benefit.

Related papers

• NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation [18.169388901224416]
  We present NKI-Agent, the first system combining domain-specific fine-tuning with agent loop for NKI kernel generation.<n>We show that tool use is critical: Opus 4.8 scores 6% in single-shot mode without agent tools. We show that an SFT-trained Qwen3-Coder-30B-A3B achieves 25.0% pass rate at 1/100th the cost of Sonnet 4 RL.
  arXiv  Detail & Related papers  (2026-07-05T16:37:41Z)
• Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering [50.72456732323037]
  We propose textbfSwarm Skills, a portable specification that extends the Anthropic Skills standard with multi-agent semantics.<n>Swarm Skills turns multi-agent into first-class, distributable assets that consist of roles, execution bounds, and a built-in semantic structure for self-evolution.<n>We show how Swarm Skills achieves zero-adapter cross-agent portability via progressive disclosure, enabling agent teams to self-evolve their coordination strategies without framework lock-in.
  arXiv  Detail & Related papers  (2026-05-11T06:26:58Z)
• Evolutionary Ensemble of Agents [7.465452178698495]
  We introduce EvE, a decentralized framework that organizes coding agents into a live, co-evolving system for algorithmic discovery.<n>By maintaining two co-evolving populations, EvE evaluates agents through a synchronous race, updating their empirical Elo ratings.<n>Compared to variants driven by a fixed initial agent or even a frozen "best-evolved" agent, EvE uniquely avoids phase mismatch.
  arXiv  Detail & Related papers  (2026-05-09T15:56:10Z)
• AVO: Agentic Variation Operators for Autonomous Evolutionary Search [38.64857334672555]
  Agentic Variation Operators (AVO) are evolutionary variation operators that replace the fixed mutation, crossover, and hand-designeds of classical evolutionary search.<n>We evaluate AVO on attention, among the most aggressively optimized kernel targets in AI, on NVIDIA Blackwell (B200)<n>AVO instantiates variation as a self-directed agent loop that can consult the current lineage, a domain-specific knowledge base, and execution feedback.
  arXiv  Detail & Related papers  (2026-03-25T16:55:04Z)
• EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience [44.734653745434834]
  We introduce EvoCUA, a native computer use agentic model.<n>Unlike static imitation, EvoCUA integrates data generation and policy optimization into a self-sustaining evolutionary cycle.<n>EvoCUA significantly outperforms the previous best open-source model, OpenCUA-72B.
  arXiv  Detail & Related papers  (2026-01-22T11:36:43Z)
• AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units [39.846358001824996]
  We propose Ascend KernelGen, a generation-evaluation integrated framework for NPU kernel development.<n>We introduce Ascend-CoT, a high-quality dataset incorporating chain-of-thought reasoning derived from real-world kernel implementations.<n>We also design NPU KernelBench, a comprehensive benchmark for assessing compilation, correctness, and performance across varying complexity levels.
  arXiv  Detail & Related papers  (2026-01-12T03:12:58Z)
• Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning [84.70211451226835]
  Large Language Model (LLM) Agents are constrained by a dependency on human-curated data.<n>We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data.<n>Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-11-20T05:01:57Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience [71.82719117238307]
  We propose SEAgent, an agentic self-evolving framework enabling computer-use agents to evolve through interactions with unfamiliar software.<n>We validate the effectiveness of SEAgent across five novel software environments within OS-World.<n>Our approach achieves a significant improvement of 23.2% in success rate, from 11.3% to 34.5%, over a competitive open-source CUA.
  arXiv  Detail & Related papers  (2025-08-06T17:58:46Z)
• Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training [67.895981259683]
  General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence.<n>Current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools.<n>We present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework.
  arXiv  Detail & Related papers  (2025-08-01T08:11:31Z)
• Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning [55.641299901038316]
  AI-generated content can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content for resource-constrained users.<n>Such a paradigm faces two significant challenges: 1) raw prompts often lead to poor generation quality due to users' lack of experience with specific AIGC models, and 2) static service provisioning fails to efficiently utilize computational and communication resources.<n>We develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation.
  arXiv  Detail & Related papers  (2025-02-17T03:05:20Z)

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.