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arXiv · 2606.09905

Toward a Small ML Runtime Stack for Raspberry Pi 5 QPUs

Yiannis Hadjiyianni·Panagiotis Michelakis·Dimitrios Stamoulis
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Abstract

We present a QPU-first ML runtime stack for Raspberry Pi 5's VideoCore VII QPU, built on top of the py-videocore7 assembly library. The system comprises reusable tiled matrix-multiplication substrate, GEMM-backed convolution, a single-head attention-style core, persistent executors, and integer execution based on smul24 instructions. For dense integer kernels, packed INT16-input with INT32 accumulation achieves nearly two orders of magnitude higher throughput over NumPy. Across operations (min/max, pooling, convolution, attention), we report improved performance over both PyTorch and NumPy. Our preliminary results indicate that Raspberry QPUs can serve as a practical execution substrate towards accelerating AI model execution at the edge.

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Yiannis Hadjiyianni, Panagiotis Michelakis, Dimitrios Stamoulis. 2026-06-06. Toward a Small ML Runtime Stack for Raspberry Pi 5 QPUs. https://arxiv.org/abs/2606.09905

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