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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint describes Deep Microcompression, a software engineering pipeline for deploying quantized and pruned CNNs on bare-metal microcontrollers with severe memory constraints. The work demonstrates technical feasibility—enabling LeNet-5 inference on an ATmega328P (2KB SRAM)—but is an unrefereed engineering contribution without peer review, comparative trials, or validation on diverse architectures or real-world use cases.
Hardware demonstration and benchmarking; single-arm technical evaluation. Bare-metal microcontroller platforms (RP2040 Cortex-M0+, ATmega328P); no human or clinical population.. Intervention: Deep Microcompression pipeline: structured pruning, quantization-aware training, and bit-packing. Compared with: TensorFlow Lite (binary size comparison only on RP2040).
55.8× weight compression ratio on LeNet-5 with 98.77% accuracy preserved 3× reduction in binary size versus TensorFlow Lite on RP2040 (Cortex-M0+) while matching accuracy First documented deployment of standard CNN on ATmega328P (2KB SRAM), previously considered infeasible
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First-in-kind engineering demonstration of CNN inference on severely constrained microcontrollers, with compression and accuracy metrics reported but no comparative efficacy trial, peer review, or clinical validation.
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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.
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