Inference Pipelines as Operating-System Objects: Priority Scheduling and Constant-Footprint Streaming for Microcontroller Neural Inference

2026-07-14

AI Summary

This work addresses the fragmented and application-level implementation of preprocessing, accelerator invocation, and postprocessing in neural inference on microcontrollers, which lacks system-wide coordination. To overcome this, the authors propose abstracting the inference pipeline as an operating system primitive and introduce SynapticOS—a runtime built atop Zephyr—that enables deterministic execution with zero heap usage and a constant memory footprint (peak: 2,784 bytes) through static memory pools, frame-level reset mechanisms, and phase-order validation. Integrated with priority-based job scheduling (real-time, normal, and best-effort) and PowerQuad DSP optimizations—including self-calibrating FFT and Q15 matrix multiplication—the system achieves 215.8 FPS (4.63 ms per frame) for face detection on the NXP FRDM-MCXN947 platform, yielding a 6.7× speedup over QEMU with software floating-point while incurring only a 20.7 KB Flash overhead and passing all 99 test cases.

Abstract

Microcontroller runtimes treat the inference pipeline -- pre-processing, accelerator invocation, post-processing -- as application code: every project re-implements stage sequencing, buffer sizing, and completion signalling around a library call. We argue these are operating-system concerns and present the Phase 2 inference engine of SynapticOS, an open-source Zephyr-based runtime that makes the pipeline a first-class OS object. A pipeline is drawn from a static pool, validated against a canonical stage order, and executed by a priority job scheduler (realtime > normal > best-effort, FIFO per class) with cancellation and a bounded job table; no heap on the inference path. Stage buffers are sized exactly from configuration and tensor geometry for the nine built-in processors (bounded 4x fallback for user stages); all intermediates live in an ephemeral arena reset per frame, so streaming footprint is constant. We evaluate on the NXP FRDM-MCXN947 (Cortex-M33, 150 MHz) and the qemu_cortex_m3 CI target, both running a deterministic stub NPU kernel: engine-overhead baselines, not silicon throughput. On the board the scheduler adds 92 us over the Phase 1 direct-HAL bracket (1,130 vs 1,038 us; dispatch 1 us); a 30-frame, six-stage face-detection pipeline averages 4.63 ms/frame (215.8 FPS, stub model included) vs 31.1 ms under QEMU soft-float, at a constant 2,784-byte arena peak returning to zero each frame. The PowerQuad DSP is routed and self-calibrated for FFT and Q15 matmul; end-to-end speedups are 5.51x (256-point FFT) and 1.66x (16x16 matmul), short of the plan's 10x target -- reported as missed, not re-scoped. Stage-boundary profiling now runs live on the board, closing a Phase 1 gap. The engine adds 3.8 KB flash on QEMU and 20.7 KB on FRDM. 99 tests across 13 ZTEST suites pass 100% under emulation. Released under Apache 2.0 at https://github.com/Dimitrios-Kafetzis/SynapticOS

Problem

• inference pipelines
• microcontroller
• operating system
• neural inference
• real-time scheduling

Innovation

• inference pipeline
• priority scheduling
• constant-footprint streaming
• microcontroller runtime
• OS-managed AI