LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

• URL: http://arxiv.org/abs/2606.10294v1
• Date: Tue, 09 Jun 2026 01:32:17 GMT
• Title: LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks
• Abstract summary: We introduce Unconventional Hardware Neural Architecture Search (UH-NAS)<n>UH-NAS is a hardware-agnostic, LLM-guided NAS framework that integrates language models as evolutionary operators to co-optimize accuracy and inference energy.<n>Tested on optical MZI hardware, UH-NAS discovers more diverse, robust architectures than conventional baselines.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision. Existing neural architecture search (NAS) methods are typically tailored to a single hardware family, limiting cross-platform comparison and generalization. We introduce Unconventional Hardware Neural Architecture Search (UH-NAS), a hardware-agnostic, LLM-guided NAS framework that integrates language models as evolutionary operators to co-optimize accuracy and inference energy. By exposing hardware as a swappable backend with per-platform energy models, physical constraints, and non-ideality simulators, UH-NAS enables fair system-level comparisons across various backends without modifying the search algorithm. Tested on optical MZI hardware, UH-NAS discovers more diverse, robust architectures than conventional baselines while outperforming existing LLM-to-NAS approaches. Additional ablations on architecture robustness under non-idealities and the role of system prompts highlight the importance of architecture-hardware co-design for emerging computing platforms.

Related papers

• Hardware Co-Design Scaling Laws via Roofline Modelling for On-Device LLMs [49.99513618431772]
  We propose a hardware co-design law that captures model accuracy and inference performance.<n>We empirically evaluate 1,942 candidate architectures on NVIDIA Jetson Orin.<n>Our architecture achieves 19.42% lower perplexity on WikiText-2.
  arXiv  Detail & Related papers  (2026-02-10T23:51:00Z)
• PEL-NAS: Search Space Partitioned Architecture Prompt Co-Evolutionary LLM-driven Hardware-Aware Neural Architecture Search [3.59587275290785]
  Hardware-Aware Neural Architecture Search (HW-NAS) requires joint optimization of accuracy and latency under device constraints.<n>We propose PEL-NAS: a search space Partitioned, architecture prompt co-Evolutionary and LLM-driven Neural Architecture Search.
  arXiv  Detail & Related papers  (2025-10-01T21:29:20Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions [121.05720140641189]
  We develop a family of models with the distilling neural architecture (DNA) techniques. Our proposed DNA models can rate all architecture candidates, as opposed to previous works that can only access a sub- search space using algorithms. Our models achieve state-of-the-art top-1 accuracy of 78.9% and 83.6% on ImageNet for a mobile convolutional network and a small vision transformer, respectively.
  arXiv  Detail & Related papers  (2024-03-02T22:16:47Z)
• Multi-objective Differentiable Neural Architecture Search [58.67218773054753]
  We propose a novel NAS algorithm that encodes user preferences to trade-off performance and hardware metrics.<n>Our method outperforms existing MOO NAS methods across a broad range of qualitatively different search spaces and datasets.
  arXiv  Detail & Related papers  (2024-02-28T10:09:04Z)
• Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric [12.52012450501367]
  Hardware-aware Neural Architecture Search (HW-NAS) is a technique used to automatically design the architecture of a neural network for a specific task and target hardware. evaluating the performance of candidate architectures is a key challenge in HW-NAS, as it requires significant computational resources. We propose an efficient hardware-aware evolution-based NAS approach called HW-EvRSNAS.
  arXiv  Detail & Related papers  (2023-11-07T11:58:40Z)
• Search-time Efficient Device Constraints-Aware Neural Architecture Search [6.527454079441765]
  Deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. We automate the construction of task-specific deep learning architectures optimized for device constraints through Neural Architecture Search (NAS) We present DCA-NAS, a principled method of fast neural network architecture search that incorporates edge-device constraints.
  arXiv  Detail & Related papers  (2023-07-10T09:52:28Z)
• You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms [10.11289927237036]
  Differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs) We introduce a lightweight hardware-aware differentiable NAS framework dubbed LightNAS, striving to find the required architecture through a one-time search. Extensive experiments are conducted to show the superiority of LightNAS over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-08-30T02:23:23Z)
• Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs [10.680700357879601]
  Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by on-device ML accelerators. Existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. We provide a two-pronged approach to this challenge: (i) a neural architecture that decouples model cost evaluation, search space design, and the algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants.
  arXiv  Detail & Related papers  (2022-04-09T00:35:19Z)
• Hierarchical Neural Architecture Search for Deep Stereo Matching [131.94481111956853]
  We propose the first end-to-end hierarchical NAS framework for deep stereo matching. Our framework incorporates task-specific human knowledge into the neural architecture search framework. It is ranked at the top 1 accuracy on KITTI stereo 2012, 2015 and Middlebury benchmarks, as well as the top 1 on SceneFlow dataset.
  arXiv  Detail & Related papers  (2020-10-26T11:57:37Z)
• Stage-Wise Neural Architecture Search [65.03109178056937]
  Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These networks consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time.
  arXiv  Detail & Related papers  (2020-04-23T14:16:39Z)

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.