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The paper introduces elasticAI.explorer, a Python framework built on Optuna for hardware-aware Neural Architecture Search (NAS). It uses a YAML-based search space specification that dynamically translates into executable neural network models, supporting layer-wise, cell-based, and hierarchical search spaces. The framework integrates hardware-specific code generation, cross-compilation, and on-device benchmarking, facilitating hardware-in-the-loop NAS workflows.
Stop wrestling with brittle, platform-specific NAS pipelines: elasticAI.explorer offers a unified, extensible framework for hardware-aware neural architecture search across diverse accelerators.
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
