Transformer-level AI running natively on MCUs was once considered impossible. Today, it is becoming a reality, with implications that extend far beyond early expectations. In this interview, Henrik Flodell of Alif Semiconductor tells EFY’s Akanksha Sondhi Gaur how the new ExecuTorch–PyTorch pipeline enables developers to deploy real-time, high-accuracy models on tiny embedded devices without architectural compromises.
Q. What Industry shifts led to this collaboration around ExecuTorch?
A. Over the last few years, there has been a surge in the number of developers seeking to deploy PyTorch-based models on embedded devices. PyTorch, being Python-driven and developer-friendly, has emerged as the preferred framework for AI research and rapid prototyping.
Until recently, however, it lacked a clean path for deploying models directly on constrained microcontrollers due to quantisation challenges, float-to-integer conversion limitations, and accuracy losses. ExecuTorch changes this.
Designed by the team behind PyTorch, ExecuTorch provides a runtime optimised for constrained systems, allowing cloud-trained models to be deployed on edge devices with minimal accuracy loss.
Q. How is collaboration between Meta, Arm, and Alif strengthening the embedded AI ecosystem?
A. Together, Meta, Arm, and Alif are enabling a direct path for deploying PyTorch models onto MCUs, supported by a unified operator backend and the industry’s first transformer-capable microcontrollers.
This collaboration delivers a scalable, open runtime tailored for edge AI, bringing the PyTorch ecosystem into embedded development.
Q. Why is PyTorch emerging as the top choice for edge developers?
A. Its rise within the embedded and edge AI community is driven by two key factors: a Python-first design aligned with data science workflows, and a mature tooling ecosystem that accelerates development.
ExecuTorch acts as the missing link, bridging the gap between cloud-scale PyTorch workflows and ultra-constrained MCUs. It allows developers to deploy cloud-trained models directly to embedded hardware without redesigning model graphs, eliminating unsupported operators, or rewriting large portions of code, dramatically simplifying the end-to-end workflow.
Q. Why ExecuTorch and not other conversion tools? What sets it apart?
A. Other methods for deploying PyTorch models on embedded hardware have existed, typically relying on conversion into formats supported by alternative ML runtimes. However, these approaches consistently struggled to preserve full model accuracy after conversion.
ExecuTorch overcomes this limitation by enabling high-fidelity conversion with minimal accuracy loss, while directly mapping PyTorch operators without requiring architectural compromises. It provides a runtime purpose-built for highly constrained embedded silicon and includes native support for quantised model deployment, which is crucial for low-power MCUs.
As an open-source project maintained by Meta, the creators of PyTorch, ExecuTorch benefits from strong ecosystem compatibility, rapid iteration, and long-term maintainability.
Q. What opportunities does this collaboration unlock?
A. This collaboration is not about redesigning semiconductors themselves, but about empowering developers with better tools. Open-source support expands the ability to reuse models across different hardware, strengthens long-term developer engagement, and ensures that deployments remain future-proof as AI frameworks evolve.
Q. How do open-source frameworks and transparency help optimise AI on low-power MCUs?
A. The industry’s shift towards running AI directly on devices rather than relying on constant cloud connectivity is driven by three major market pressures.
First, battery life has become critical. Wireless radios consume significant power, so minimising cloud communication dramatically extends device runtime.
Second, latency is a defining factor in the user experience. Cloud round-trips introduce delays that make AI responses feel sluggish and disconnected from real-time interactions.
Third, cost continues to rise, as storing and transmitting large volumes of sensor data to the cloud becomes increasingly expensive for manufacturers and service providers.
In this context, open frameworks play an essential role. They enable broader ecosystem collaboration, accelerate development cycles, and reduce long-term risk for device makers. When paired with licensable hardware platforms such as Arm’s Ethos NPUs, these open standards ensure that AI workloads can run efficiently at the edge while remaining aligned with evolving industry tools and innovations.
Q. How does open source reduce development time and cost for embedded engineers?
A. Because AI is advancing at an unprecedented pace, proprietary toolchains cannot evolve quickly enough to remain relevant. Open frameworks such as PyTorch, paired with ExecuTorch, offer long-term ecosystem support, rapid community-driven improvements, and significantly lower redesign risks as standards evolve.
They also ensure broad cross-vendor compatibility, giving developers the freedom to move across hardware platforms without rebuilding their entire stack. By investing in open ecosystems, companies effectively derisk development, avoiding vendor lock-in that can derail long-term product strategies and force costly re-engineering efforts.
Q. What workflow simplifications does PyTorch + ExecuTorch introduce?
A. Previously, deploying a PyTorch model on an MCU required engineers to rework neural network graphs, replace unsupported operators, and often accept noticeable accuracy compromises to fit within device constraints. With ExecuTorch, much of this complexity is removed. Data scientists can train models in PyTorch as usual, and embedded engineers can deploy them directly on the target hardware without modifying the model’s structure or logic. This streamlines the workflow while preserving the integrity of the original model.
Q. What new design capabilities are becoming possible on constrained devices?
A. ExecuTorch makes it possible to run essentially the same model from server-class systems down to wearable-grade hardware without restructuring or downgrading it. This capability extends to devices such as smart rings, health trackers, fitness bands, and a wide range of industrial sensors. Until now, full-fidelity model deployment on such constrained platforms was rarely feasible; ExecuTorch addresses this limitation.
Q. How can the community contribute to ExecuTorch?
A. ExecuTorch is fully open on GitHub, giving developers the freedom to use and extend the runtime as needed. They can add support for new hardware backends, introduce additional operators, and optimise the runtime for specific workloads or silicon platforms.
Meta announced ExecuTorch’s general availability at the PyTorch Conference. Following this, Alif, in collaboration with Arm, enabled backend support for the Ethos-U55 and Ethos-U85 NPUs. This joint effort allows PyTorch models to be deployed on Alif’s Ensemble and Balletto MCU families, widening access to edge-ready AI.
Q. As embedded devices evolve into true AI systems, what challenges still limit full on-device intelligence?
A. Embedded devices are becoming real AI systems, but only within tight physical limits. To make advanced models run locally, engineers must navigate severe memory constraints, the high energy cost of moving data, restricted model footprints, and limited internal bandwidth. These challenges make it impractical to deploy cloud-scale models directly.
Instead, teams must carefully optimise by trimming data sizes, pruning unnecessary layers, and tailoring architectures to specific tasks. The result is a hybrid model in which most intelligence runs on-device, while only infrequent, compute-heavy workloads are offloaded to the cloud.
Q. What design practices help engineers build AI devices within severe constraints?
A. Building efficient AI-driven embedded devices requires navigating both technical and organisational constraints. On the technical side, teams must optimise models for the intended task, reduce data resolution where possible, for example, by using machine-vision-grade cameras instead of high-resolution sensors, and minimise memory consumption throughout the pipeline.
There are also team-level challenges. Modern embedded AI development typically requires a multidisciplinary group, including data scientists to refine datasets, hardware designers, firmware engineers, and, in some cases, cloud engineers. The era when a single engineer could manage everything from hardware to AI modelling has largely passed.
Q. What compression and quantisation techniques work best?
A. INT8 quantisation is the industry standard for deploying AI on constrained hardware. It reduces model size by approximately fourfold, enables faster computation, and lowers power consumption, making it well suited for NPUs such as the Ethos-U55 and Ethos-U85.
While ExecuTorch does not perform the quantisation step itself, it ensures that quantised models execute efficiently during inference, allowing developers to take full advantage of optimised formats on embedded devices.
Q. How do developers validate performance for embedded AI?
A. Developers validate embedded AI performance by measuring accuracy on proprietary datasets, assessing latency under real operating conditions, and evaluating overall robustness across different scenarios. They also use synthetic data augmentation, such as generating licence-plate variations with different angles, lighting conditions, or distortions, to strengthen edge-case performance and ensure reliable behaviour in diverse real-world environments.
Q. What model types demonstrate ExecuTorch on Alif MCUs most effectively?
A. Two recent demonstrations illustrate what ExecuTorch enables on Alif’s MCU platforms.
Conformer Speech-to-Text (PyTorch → MCU in under two weeks): A Conformer-based speech-to-text model was trained on the open-source LibreVoice dataset and deployed directly on Alif’s MCU hardware. The system performs real-time speech transcription using onboard microphones and displays converted text instantly, demonstrating how complex audio models can now run locally on deeply constrained devices.
Llama-2 generative model: In this vision-to-story demonstration, a camera captures an image, an onboard classifier identifies objects in the frame, and a locally running Llama-2 model generates a narrative based on the scene. This pipeline—from camera input to classification to generative response—shows how embedded generative AI can support interactive toys, educational tools, and intelligent assistants capable of creating context-aware stories entirely on-device.
Q. What hardware accelerators enable PyTorch/ExecuTorch on Alif MCUs?
A. Alif uses Arm’s Ethos-U85, the latest-generation NPU, alongside the Ethos-U55 to deliver hardware acceleration for a wide range of neural network architectures, including CNNs, RNNs, and modern transformer models. With full operator coverage across these network types, the NPUs deliver inference performance more than two orders of magnitude faster than CPU-only execution, making advanced AI practical on highly constrained embedded devices.
Q. Any measurable performance gains compared to other runtimes?
A. Direct performance comparisons vary depending on the model and workload. However, Alif’s MCUs demonstrate more than a 100× speed-up compared with traditional microcontrollers without NPUs. Full hardware acceleration delivers strong performance per watt, improving efficiency for AI inference on constrained edge devices.
Q. How do Alif’s SDKs and tools bridge model training to on-device deployment?
A. Alif does not provide datasets, as deployment environments differ widely across applications, but it supports developers throughout the rest of the workflow. This includes compatibility with PyTorch and ExecuTorch, as well as NPUs, drivers, and tightly integrated MCU support.
Developers can train models on domain-specific data, while Alif provides example projects and reference designs to bridge the gap between training and on-device deployment.
Q. How is generative AI reshaping embedded applications?
A. Generative AI on embedded hardware enables new use cases, including real-time speech-to-text processing, conversational toys, contextual personal assistants, and interactive voice systems. Because transformer models incorporate a form of memory, they can maintain context across multiple interactions, enabling multi-turn conversations entirely on-device.
Q. What challenges arise when running generative models fully on-device?
A. Running generative models fully on-device introduces challenges, including strict power limits, thermal constraints, and limits on locally stored conversational context. Despite these hurdles, the benefits include near-zero latency and improved privacy, as user data remains on the device.
Q. How does Alif achieve high AI performance while keeping power low?
A. Alif achieves this balance through a split-silicon architecture that separates responsibilities across two domains. A high-efficiency domain manages always-on sensing and low-power machine-learning triggers, handling continuous data collection. A high-performance domain activates only when more complex analysis is required, delivering rapid processing without unnecessary power draw.
Q. How do PyTorch and ExecuTorch support Alif’s vision of bringing generative AI from cloud to edge?
A. They support this by streamlining, repeating, and improving workflows, while expanding access to advanced AI development for mainstream embedded developers.
Q. How does this integration strengthen Alif’s competitive strategy?
A. Alif has built a track record of industry firsts, from introducing the first MCU with an integrated NPU in 2021 to enabling hardware acceleration for transformer networks. Integrating ExecuTorch strengthens this position, reinforcing leadership in edge AI while opening access to developers who rely on PyTorch workflows.
Q. What new markets will open as PyTorch-based AI becomes MCU-ready?
A. This shift enables applications across wearables, healthcare devices, smart rings, fitness bands, industrial IoT systems, machine-vision sensors, and consumer electronics. Any sensor-equipped device can now integrate local intelligence, bringing advanced AI capabilities directly to the edge.
Q. Where is the MCU-based AI market heading in the next few years?
A. AI acceleration is expected to become increasingly common in microcontrollers, much like sensor interfaces today. Local inference, once a niche requirement, is likely to become a baseline expectation in next-generation embedded systems.
Q. Will ExecuTorch become the standard runtime for edge AI, as PyTorch dominates cloud AI?
A. It appears likely, unless a more effective alternative emerges. At present, ExecuTorch is on a clear trajectory towards wider industry adoption.
