Poster

ExecuTorch - A Unified PyTorch Solution to Run ML Models On-Device

Chen Lai ⋅ Cemal Bilgin ⋅ ⋅ Gregory Comer ⋅ ⋅ ⋅ ⋅ Lucy Qiu ⋅ Mengwei Liu ⋅ ⋅ Songhao Jia ⋅ ⋅ ⋅ Digant Desai ⋅ Hansong Zhang ⋅ Manuel Candales ⋅ Scott Roy ⋅ Sicheng Jia ⋅ Mergen Nachin ⋅ ⋅ Yanan Cao ⋅ ⋅ Shunting Zhang ⋅ ⋅ Angela Yi ⋅ Zhenrui Zhang ⋅ Andrew Or ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ Supriya Rao ⋅ ⋅ Soumith Chintala

Project Page [ OpenReview]

Abstract

Local execution of AI on edge devices is critical for privacy, low latency, and offline operation. However, deploying models on diverse hardware remains fragmented, often requiring model conversion or complete implementation outside the PyTorch ecosystem where the model was originally authored. We introduce ExecuTorch, a unified PyTorch-native deployment framework for edge AI. ExecuTorch enables seamless deployment of machine learning models across heterogeneous compute environments. It scales from completely embedded microcontrollers to complex system-on-chips (SoCs) with dedicated accelerators, powering devices ranging from wearables and smartphones to large compute clusters. ExecuTorch preserves PyTorch semantics while allowing customization, support for optimizations like quantization, and pluggable execution ''backends''. These features together enable fast experimentation, allowing researchers to validate deployment behavior entirely within PyTorch, bridging the gap between research and production.

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