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

arXiv: https://arxiv.org/abs/2604.09124 · HTML: https://mlsanity.com/paper/2604.09124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration](https://mlsanity.com/paper/1911.09925/) (2019)
- [Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators](https://mlsanity.com/paper/1809.04070/) (2018)
- [Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance](https://mlsanity.com/paper/2110.15238/) (2021)
- [Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads](https://mlsanity.com/paper/2412.11702/) (2024)
- [Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollers](https://mlsanity.com/paper/2408.04413/) (2024)
- [Map-and-Conquer: Energy-Efficient Mapping of Dynamic Neural Nets onto Heterogeneous MPSoCs](https://mlsanity.com/paper/2302.12926/) (2023)
- [Cheshire: A Lightweight, Linux-Capable RISC-V Host Platform for Domain-Specific Accelerator Plug-In](https://mlsanity.com/paper/2305.04760/) (2023)
- [MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devices](https://mlsanity.com/paper/2410.08855/) (2024)
- [Spatz: Clustering Compact RISC-V-Based Vector Units to Maximize Computing Efficiency](https://mlsanity.com/paper/2309.10137/) (2023)
- [HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms](https://mlsanity.com/paper/2406.07453/) (2024)
- [Hybrid Modular Redundancy: Exploring Modular Redundancy Approaches in RISC-V Multi-Core Computing Clusters for Reliable Processing in Space](https://mlsanity.com/paper/2303.08706/) (2023)

## Abstract (arXiv)

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.