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

Opegbemi Matthias Busoye; Tolulope Matthew Busoye; Eghonghon-aye Eigbe

cs.LGSep 4, 2026

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

Authors: Opegbemi Matthias Busoye, Tolulope Matthew Busoye, Eghonghon-aye Eigbe

Organizations: PowerLabs Technologies, Lagos, Nigeria

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.

Figures & tables

Figure 1: Deep microcompression: Development Pipeline

Figure 2: Deep microcompression: Inference Pipeline

Model Config	Flash Memory (B)		SRAM Usage (B)			#MACs	#BOPs	Latency (s)	Energy (J)	Accuracy (%)
Model Config	Weights	Code	Global Data	Workspace	Stack	#MACs	#BOPs	Latency (s)	Energy (J)	Accuracy (%)
I. Target Deployment (Hardware: ATmega328P)
DMC-Tiny (Ours)	16,384	10,578	1,588	1,296	78	131,040	1,619,428	2.80	0.308	98.17
II. Comparative Benchmark (Proxy Hardware: RP2040/Pico) †
Baseline (FP32)	148,242	43,604	25,288	5,880	532	392,040	0	5.58	15.624	99.42
DMC-Ultra	2,640	43,876	7,712	5,880	544	226,740	2,269,400	0.15	0.431	98.77

Table 1: Performance Comparison: Deployment Feasibility & Benchmarking

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Configuration	Flash Memory (B)		SRAM Usage (B)			#MACs	#BOPs	Latency (s)	Energy (J)	Accuracy (%)
Configuration	Weights	Code	Global Data	Workspace	Stack Peak	#MACs	#BOPs	Latency (s)	Energy (J)	Accuracy (%)
I. Compiler Optimization (Model: DMC-Ultra, Pack: 4-bit)
avr-gcc -O0 (None)	2,638	17,484	6,158	5,880	83	226,740	2,269,400	9.37	2.76	98.77
avr-gcc -O3 (Speed)	2,638	13,566	6,168	5,880	64	226,740	2,269,400	4.77	1.41	98.77
avr-gcc -Os (Size)	2,638	10,712	6,172	5,880	69	226,740	2,269,400	5.07	1.50	98.77
II. Bit-Packing Impact (Model: DMC-Ultra, Compiler: -Os)

Appendix

Table 2: Ablation Study: Impact of Toolchain and Bit-Packing (Hardware: ATmega2560)

Float32 Baseline
Framework	Flash (KB)	RAM (KB)	Worksp. (KB)	Latency (s)	Energy (J)	Acc (%)
TFLite Micro (FP32)	365.2	27.4	24.7	0.60	0.13	98.91
DMC (FP32)	199.4	4.5	22.97	0.56	0.12	98.91
Int8 Quantized
TFLite Micro (Int8)	254.9	10.7	8.4	0.055	0.012	98.88
DMC (Int8)	86.4	7.7	5.7	0.226	0.05	98.89

Appendix

Table 3: Benchmark: DMC vs. TFLite Micro (LeNet-5 on RP2040/Pico)

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The dominant trajectory of modern machine learning has been to scale up: larger models, larger accelerators, larger memory budgets. Yet a multi-year global semiconductor supply constraint and the growing energy and carbon cost of always-online inference expose the fragility of this trajectory and motivate the opposite direction: refactoring AI and ML algorithms to fit the small, ubiquitous microcontrollers already in mass production in wearables, sensors, and edge appliances. We present an end-to-end open-source reproduction of FastGRNN, a compact gated recurrent cell, deployed on two bare-metal targets: the 8-bit Arduino (ATmega328P) and the 16-bit MSP430 (no hardware multiplier; 16 KB Flash; 512 B SRAM). Our compression pipeline combines low-rank weight factorization, iterative hard-thresholding sparsity, and per-tensor Q15 post-training quantization with explicit activation calibration. The deployed model occupies 566 bytes of weights and achieves macro F1 = 0.918 (seed 0; five-seed Q15 mean 0.853+-0.107) on the HAPT test set. It matches a PyTorch reference at 100% prediction agreement across 3,399 test windows (MCU seed 0; 99.91-100% C-equivalent across five seeds). Both platforms sustain real-time 50 Hz streaming inference (9.21 ms per sample on Arduino; 13 ms on MSP430), where a 256-entry sigmoid/tanh look-up table delivers a 30.5x speedup on the multiplier-less MSP430. Four contributions extend the original FastGRNN paper: (i) cross-platform bit-equivalent deterministic inference; (ii) characterization of recurrent warm-up latency (median 74 samples, 1.48 s; worst-case 125 samples, 2.50 s over 100 test windows); (iii) a deployable look-up-table recipe for multiplier-less embedded targets; and (iv) hardware energy characterization showing 17.7 mW active inference power, <0.09 mW idle power, and 96.7% energy reduction with the LUT.

Abstract

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