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arXiv · 2604.09124

MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs

Enrico Russo·Mohamed Amine Hamdi·Alessandro Ottaviano·Francesco Conti·Angelo Garofalo·Daniele Jahier Pagliari·Maurizio Palesi·Luca Benini·Alessio Burrello
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Abstract

Deploying DNNs on System-on-Chips (SoC) with multiple heterogeneous acceleration engines is challenging, and the majority of deployment frameworks cannot fully exploit heterogeneity. We present MATCHA, a unified DNN deployment framework that generates highly concurrent schedules for parallel, heterogeneous accelerators and uses constraint programming to optimize L3/L2 memory allocation and scheduling. Using pattern matching, tiling, and mapping across individual HW units enables parallel execution and high accelerator utilization. On the MLPerf Tiny benchmark, using a SoC with two heterogeneous accelerators, MATCHA improves accelerator utilization and reduces inference latency by up to 35% with respect to the the state-of-the-art MATCH compiler.

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Enrico Russo, Mohamed Amine Hamdi, Alessandro Ottaviano, Francesco Conti, Angelo Garofalo, Daniele Jahier Pagliari, Maurizio Palesi, Luca Benini, Alessio Burrello. 2026-04-10. MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs. https://doi.org/10.1145/3770743.3804120

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