elasticAI.explorer: Towards a Unified End-to-End Framework for Hardware-Aware Neural Architecture Search

2026-05-28

AI Summary

This work proposes a hardware-aware, extensible neural architecture search (NAS) framework that decouples the search, optimization, and deployment pipelines to overcome the tight coupling prevalent in existing NAS methods, which hinders adaptation to new hardware or custom operators. The framework employs YAML for unified search space specification and leverages Optuna for efficient architecture search. It supports hardware-in-the-loop evaluation by integrating Docker-based cross-compilation, automated on-device binary generation, and multidimensional performance metrics—including FLOPs, parameter count, and latency—thereby significantly enhancing support for heterogeneous accelerator platforms and reducing engineering overhead in embedded AI deployment.

Abstract

Neural Architecture Search (NAS) has become an important approach for automatically designing neural networks under task-specific and hardware-specific constraints. However, many existing NAS frameworks tightly couple search space definitions, model implementations, and deployment pipelines, making extension to new hardware platforms and custom operators difficult. In this paper, we present the elasticAI.explorer, an extensible Python framework for hardware-aware NAS built on top of Optuna. The framework introduces a YAML-based search space specification that dynamically translates into executable neural network models during sampling. The approach supports layer-wise, cell-based, and hierarchical search spaces while maintaining a unified interface for optimization and deployment. Beyond architecture generation, the framework integrates hardware-specific code generation, Docker-based cross-compilation toolchains, and automated creation of on-device benchmarking binaries, enabling hardware-in-the-loop NAS workflows. The system further provides extensible evaluators for FLOPs, parameter count, and latency estimation. The elasticAI.explorer aims to reduce the engineering overhead of embedded AI deployment and accelerate research on hardware-aware NAS for heterogeneous accelerator platforms

Problem

• Neural Architecture Search
• hardware-aware
• search space
• deployment
• extensibility

Innovation

• Hardware-aware NAS
• YAML-based search space
• Hardware-in-the-loop
• Cross-compilation toolchain
• Extensible framework