arXiv AI
Toward Agentic AI for the 6G Physical Layer: From Algorithm Discovery to Verified Baseband Silicon
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arXiv AI
Sep 11
KernelGenBench: Can LLMs and Agents Write Efficient Kernels Across Operator Sources and Hardware Platforms?
KernelGenBench is a unified benchmark that evaluates large language models and agentic systems for generating efficient Triton kernels across diverse operator sources and hardware platforms. It covers 210 operators from PyTorch ATen, vLLM, and cuBLAS, and tests a 110‑operator subset on six different chips, consuming over 15 billion tokens in evaluation. The study finds that no single method dominates across all sources and platforms, with significant variations in correctness and performance depending on the operator source and hardware, and that agentic approaches require millions of tokens per successful operator.
arXiv AI
Oct 15
Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization
Agentic Architect is a new framework that uses large language models to automatically evolve and optimize CPU microarchitectural designs. Starting from a seed design, the LLM proposes code changes, which are compiled, simulated, and evaluated against performance, area, and power constraints, with results feeding back into subsequent iterations. The framework was tested on prefetching, cache replacement, and branch prediction, achieving notable speedups and area reductions compared to state‑of‑the‑art designs.
arXiv AI
Oct 7
Agentic Design Space Exploration for Joint Hardware Configuration Selection and Mapping of AI Inference Workloads on Heterogeneous Edge SoCs
The paper introduces TraceDSE, an agentic design space exploration framework for jointly mapping AI inference workloads to heterogeneous edge SoCs and configuring each processing unit. Unlike traditional black-box optimization, TraceDSE uses a proposer‑critic loop powered by large language models and enriched with system execution traces to identify bottlenecks and refine design choices. Experiments on an Intel Meteor Lake SoC show that TraceDSE outperforms state‑of‑the‑art evolutionary and Bayesian methods, improving Pareto frontier hypervolume by up to 68% while reducing hardware evaluations by 6–9×.
arXiv AI
Aug 28
Redwood: A Frontier AI Accelerator Designed, Verified, and Deployed from Scratch in 2 Weeks by AI
arXiv:2608. 26418v1 Announce Type: cross Abstract: Modern AI workloads and the hardware that runs them evolve on different timescales: architectural definition precedes volume silicon by years, while target workloads shift in months.
arXiv Machine Learning
Aug 27
Beyond Scaling: Self-Evolving LLM Agents for Hardware Kernel Optimization via an Experience-Driven Workflow and Experience Graph Memory
The paper introduces KOPE, an experience‑driven framework that records hardware kernel optimization trajectories in an Experience Graph Memory and uses Active Context Management and Injection to retrieve relevant past decisions under a fixed token budget. KOPE preserves decision order, outcomes, and alternative branches, enabling evidence from completed runs to inform future optimization steps. In experiments, KOPE achieves a 1.54× speedup over the strongest baseline, raises pass rates from 60.0% to 84.6%, and reduces token consumption dramatically, demonstrating the benefits of continual learning from external experience while keeping the foundation model unchanged.
arXiv Machine Learning
Jul 31
KernelGenBench: A Multi-Source and Multi-Chip Benchmark for LLM-based Kernel Generation
arXiv:2607. 27231v1 Announce Type: cross Abstract: Large language models (LLMs) have significantly increased the demand for efficient accelerator kernels, but kernel development remains a highly specialized and labor-intensive task.