arXiv:2604.18616v2 Announce Type: replace-cross Abstract: LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires co...
arXiv:2606. 25207v1 Announce Type: new Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget.
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.
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×.
Large Language Model agents are being explored for chip design, but most methods work directly at RTL. This study compares Direct RTL Design, Agent-based HLS Design, Post-Compiler HLS Refinement, and Post-HLS RTL Refinement, and proposes a combined workflow called Agent-based HLS with RTL Refinement (AHRR). Using FPGAs for evaluation, AHRR achieves a 2.6× geometric‑mean speedup over Direct RTL Design across an 11‑task benchmark suite, demonstrating that higher‑level abstractions and subsequent RTL refinement can improve chip design efficiency.
The paper investigates whether AI agents truly understand computer architecture by comparing their performance when the same 15‑dimensional accelerator design space is presented either as meaningful architectural knobs or as anonymous variables. Using the AutoTuring framework, the authors find that agents with architectural knowledge outperform blind agents on a nine‑kernel FP16 GEMM benchmark, yet a critic loop can largely recover this advantage. The study highlights that architectural knowledge and structured critique act as substitutes rather than complements in improving agent performance.
arXiv:2608. 12629v1 Announce Type: new Abstract: GPU kernel agents and GPU programming languages have advanced separately, leaving expert kernels difficult to reproduce.
The paper introduces AgenticBBO-Bench, a cross‑domain benchmark for evaluating large language model agents on black‑box optimization tasks such as synthetic functions, hyperparameter tuning, database tuning, chip design, and molecular design. Experiments show that agentic BBO outperforms direct LLM‑based methods in all five domains and beats the best numerical optimizers in four. The study also analyzes the impact of optimization tools, task semantics, prior knowledge, and the LLM’s role, finding that numerical tools are not consistently helpful, task semantics are broadly useful, and numerical optimizers can capitalize on agent‑generated search trajectories.
arXiv:2511. 22651v2 Announce Type: replace-cross Abstract: Optimization methods have long advanced many fields, yet they struggle when faced with design problems where the search space and design parameters are difficult to define.
The paper introduces the concept of substrate blindness, where AI agents lack execution context in their planning. By providing a 128 MB RAM and 10 s wall‑time contract to large language models, the authors show that agents generate code that uses less memory, runs faster, and incorporates structural changes such as bounded blocking and in‑place buffers. Across three leading models, contract disclosure improved resource usage and correctness, demonstrating that minimal execution contracts can guide agents to produce more efficient programs.
arXiv:2610.38329v1 Announce Type: cross Abstract: Frontier large language models write correct CUDA kernels for most single-operator benchmark tasks, yet their accuracy collapses on accelerators whos...
Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot establish why. An agent that improves an accelerator...