SynapticOS: An Inference-First Runtime Architecture for Neural Processing Units on Resource-Constrained Microcontrollers

2026-07-14

AI Summary

This work addresses the limitations of existing microcontroller systems that treat AI inference merely as a conventional application library, leading to memory fragmentation, complex hardware state management, and poor control over model lifecycles. To overcome these challenges, the authors propose the first open-source runtime architecture that natively treats AI inference as a first-class workload, built upon the Zephyr RTOS. The design integrates a tensor-aware bump allocator, a four-state abstraction layer for NPU/DSP resources, a three-state model registry, and a four-tag cycle-accurate profiler. This enables zero-fragmentation tensor allocation, deterministic hardware access, secure model hot-swapping, and fine-grained performance profiling. Evaluated on the NXP MCXN947 platform, the system achieves 78,000 constant-time tensor allocations per second, an end-to-end inference overhead of only 1,038 microseconds, and 100% CI test pass rate, with all code publicly released.

Abstract

Microcontrollers with on-die neural processing units (NPUs) have become mainstream, but the system software hosting them has not: production combinations of Zephyr or FreeRTOS with TensorFlow Lite Micro treat AI inference as an application-layer library, leaving memory fragmentation, accelerator-state hygiene, and model-lifecycle guards as recurring application-developer concerns. We present the Phase 1 foundation of SynapticOS, an open-source runtime built on Zephyr that treats inference as a first-class workload. It contributes four cooperating subsystems: (1) a tensor-aware bump allocator with 16-byte DMA-aligned persistent and ephemeral lifetimes sharing a single arena, achieving constant-time allocation (~154 cycles per call, ~78,000 allocations per second at 150 MHz, invariant across tensor sizes) with zero fragmentation by construction; (2) a four-state hardware abstraction layer for the NPU and DSP, implemented by a deterministic software stub (for CI under QEMU) and a Neutron-flavoured backend (for the NXP MCXN947); (3) a three-state model lifecycle registry with duplicate-name detection, idempotent load/unload, and hot-swap guards; and (4) a four-mark cycle-accurate profiler. We evaluate on the NXP FRDM-MCXN947 (dual Cortex-M33 at 150 MHz) and the qemu_cortex_m3 emulator. Build footprints are 67 KB flash / 184 KB SRAM on FRDM (shell, 128 KB arena) and 24 KB flash / 28 KB SRAM on QEMU (no shell, 8 KB arena). End-to-end inference brackets through the deterministic stub kernel measure 1,038 us on FRDM and 781 us on QEMU for a 16x16x3 INT8 input; these are baseline overhead numbers, not Neutron silicon measurements, which arrive with the real SDK invoke path in Phase 2. A 61-test suite across 10 ZTEST suites passes 100% in 6.6 s on the CI emulator path. SynapticOS is released under Apache 2.0 at https://github.com/Dimitrios-Kafetzis/SynapticOS

Problem

• microcontrollers
• neural processing units
• AI inference
• memory fragmentation
• model lifecycle

Innovation

• inference-first runtime
• tensor-aware allocator
• zero-fragmentation memory
• NPU hardware abstraction
• model lifecycle management