Entropy Sampling-Based Neural Architecture Search for Resource-Constrained Microcontroller Targets

Heidorn C, Hannig F, Riedelbauch D, Strohmeyer C, Teich J (2026)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2026

Conference Proceedings Title: Proceedings of the Conference on Design, Automation and Test in Europe (DATE)

Event location: Verona IT

Abstract

Neural architecture search (NAS) is a popular approach for the exploration of neural network (NN) architectures. Recently proposed hardware-aware NAS techniques even take resource constraints, such as FLOP count and number of weights, into account. Still, in typical NAS search spaces, a significant portion of candidate NNs may be infeasible when it comes to satisfying tight memory (i.e., RAM and ROM) and timing constraints, particularly in the case of microcontroller targets. As evaluating each design point can be quite time-intensive, we first show how to pre-process a given design space to a reduced set of only feasible (resource constraint fulfilling) solutions, and then efficiently sampling from this set of only feasible solutions by proposing an entropy-based sampling technique and the optimization goal to maximize accuracy. We demonstrate that our approach is able to find feasible solutions with similar accuracy to other hardware-aware NAS techniques, but already after a much lower number of model evaluations, with examples taken from the MLPerf Tiny Benchmark suite.

Authors with CRIS profile

Christian Heidorn Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Frank Hannig Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Jürgen Teich Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)

Related research project(s)

Optimization and Toolchain for Embedding AI (OpTC) March 1, 2023 - Feb. 28, 2026

Involved external institutions

Schaeffler Technologies AG & Co. KG DE Germany (DE)

How to cite

APA:

Heidorn, C., Hannig, F., Riedelbauch, D., Strohmeyer, C., & Teich, J. (2026). Entropy Sampling-Based Neural Architecture Search for Resource-Constrained Microcontroller Targets. In Proceedings of the Conference on Design, Automation and Test in Europe (DATE). Verona, IT.

MLA:

Heidorn, Christian, et al. "Entropy Sampling-Based Neural Architecture Search for Resource-Constrained Microcontroller Targets." Proceedings of the Conference on Design, Automation and Test in Europe (DATE), Verona 2026.

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