ELASTIC: Efficient Once for All Iterative Search for Object Detection on Microcontrollers

Deploying high-performance object detectors on TinyML platforms poses significant challenges due to tight hardware constraints and the modular complexity of modern detection pipelines. Neural Architecture Search (NAS) offers a path toward automation, but existing methods either restrict optimization to individual modules—sacrificing cross-module synergy—or require global searches that are computationally intractable. We propose ELASTIC (Efficient Once for AlLIterAtiveSearch for ObjecTDetectIon on MiCrocontrollers), a unified, hardware-aware NAS framework that alternates optimization across modules (e.g., backbone, neck, and head) in a cyclic fashion. ELASTIC introduces a novelPopulation Passthroughmechanism in evolutionary search that retains high-quality candidates between search stages, yielding faster convergence, up to an 8% final mAP gain, and eliminates search instability observed without population passthrough. In a controlled comparison, empirical results show ELASTIC achieves +4.75% higher mAP and 2× faster convergence than progressive NAS strategies on SVHN, and delivers a +9.09% mAP improvement on PascalVOC given the same search budget. ELASTIC achieves 72.3% mAP on PascalVOC, outperforming MCUNET by 20.9% and TinyissimoYOLO by 16.3%. When deployed on MAX78000/MAX78002 microcontrollers, ELASTICderived models outperform Analog Devices’ TinySSD baselines, reducing energy by up to 71.6 %, lowering latency by up to 2.4×, and improving mAP by up to 6.99 percentage points across multiple datasets. The experimental videos and codes are available on the project website1.