Deep Microcompression: Structured Pruning and Bit-packed Quantization for Microcontrollers

Abstract:This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on bare-metal microcontrollers. DMC integrates structured pruning, quantization-aware training, and fixed-length bit-packing to achieve a 55.8$\times$ weight compression ratio on LeNet-5 (98.77\% accuracy), generating a dependency-free C library with deterministic latency. On the RP2040 (Cortex-M0+), DMC reduces binary size by 3$\times$ versus TensorFlow Lite while matching its accuracy. Critically, DMC enables the first documented deployment of a standard CNN on the ATmega328P, a device constrained to 2KB SRAM, previously considered infeasible for CNN inference.

Machine Learning (cs.LG)

arXiv:2609.05081 [cs.LG]

(or arXiv:2609.05081v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.05081 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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