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ExecuTorch has recently matured into a production ready framework designed specifically for efficient edge deployment of PyTorch models. Its architecture supports a broad spectrum of hardware targets—from low power, bare metal or RTOS based microcontrollers (MCU) to higher performance Linux or Android based microprocessor platforms—while meeting the demanding constraints of memory, compute, and power typically found in real world embedded applications. This talk focuses on the deployment flexibility ExecuTorch offers for MCU class devices, highlighting how different backends enable efficient execution across heterogeneous compute units. We will explore CPU, DSP, and NPU acceleration paths using the Cortex-M, Cadence, Ethos-U, and eIQ Neutron backends, and discuss how these integrate into typical ML model deployment workflows. To make the session practical and application oriented, we will present an optimization journey aimed at reducing power consumption—an essential requirement for ML workloads in energy constrained environments. Attendees will gain insights into backend selection, performance trade offs, and best practices for suitable deploying PyTorch models on edge devices.
Principal AI/ML Engineer at NXP Semiconductors, NXP Semiconductors
Robert Kalmar is a Principal Machine Learning Engineer at NXP Semiconductors. He received his master’s degree in machine learning and intelligent systems from Brno University of Technology. At NXP he focus on machine learning solution enablement for embedded and mobile devices... Read More →
I hold a Master’s degree in Machine Learning from the Brno University of Technology, graduating with distinction at both bachelor’s and master’s levels. I am a mid-level AI/ML Software Engineer at NXP Semiconductors with 2.5+ years of experience. I won the 2025 iGEM overgraduate... Read More →
