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

• URL: http://arxiv.org/abs/2604.09124v1
• Date: Fri, 10 Apr 2026 09:04:34 GMT
• Title: MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs
• Abstract summary: MATCHA generates highly concurrent schedules for parallel, heterogeneous accelerators.<n>On the Tinyerf benchmark, MATCHA improves accelerator utilization and reduces latency by up to 35%.
• Score: 45.487954219664026
• License: http://creativecommons.org/licenses/by/4.0/
• 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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