Lightweight ML for Embedded Systems Built for Real Silicon

Under Services → TinyML & Edge AI → Lightweight ML for Embedded Systems, WorkSprout ships inference on constrained hardware — INT8 pipelines, RTOS ports, sensor fusion, and OTA-ready firmware your embedded team can maintain after handoff.

Lightweight ML for Embedded Systems TinyML Solutions AI Engine Integration Edge AI Deployment Edge Model Optimization

Lightweight ML for Embedded Systems

Model selection, quantization, and deployment pipelines for microcontrollers and embedded targets — accurate inference within tight memory and power budgets.

TinyML Solutions

End-to-end TinyML programmes — sensor fusion, on-device training workflows, and production firmware integration for real-world edge products.

AI Engine Integration

Integrate TensorFlow Lite, ONNX Runtime, and vendor NPUs into existing firmware and application layers with stable APIs and observability.

Edge AI Deployment

Field deployment of edge AI — OTA update paths, device fleets, monitoring, and rollback strategies for production edge inference.

Edge Model Optimization

Pruning, distillation, INT8 quantization, and kernel tuning so models meet latency and energy targets on target silicon.

FieldSense

Lightweight ML

Q3 – Q4 2025

WorkSprout Edge Team

FieldSense: On-Device Vibration ML on STM32 for Predictive Maintenance at the Edge

WorkSprout partnered with FieldSense to move a cloud-trained vibration classifier onto STM32 class hardware — under 128 KB RAM, sub-15 ms inference, and OTA model slots so their fleet could receive updated weights without truck rolls.

TensorFlow Lite CMSIS-NN Edge Impulse FreeRTOS STM32 Grafana

12 Wk.Prototype to Fleet

<15msOn-Device Latency

4×Model Size Cut

100%Client Satisfaction

Embedded ML Capabilities

What WorkSprout delivers for lightweight ML engagements — from quantized graphs and RTOS integration to production firmware hooks on real boards.

<15ms Typical on-device latency

Quantized model pipelines

INT8 and mixed-precision graphs sized for SRAM and flash on MCUs.

RTOS & bare-metal ports

FreeRTOS, Zephyr, and vendor SDK integration with deterministic inference loops.

Sensor fusion at the edge

IMU, vibration, and analog front-ends fused on-device before optional cloud upload.

Power-aware scheduling

Duty-cycled inference that preserves battery life on field hardware.

Production firmware hooks

OTA-ready bundles with versioned model slots and rollback paths.

Bench-to-field validation

Latency, memory, and accuracy gates on real silicon before ship.

Explore TinyML services

What You Get with Lightweight ML

Production-ready firmware artefacts and validated model bundles — not notebook exports — so your hardware team can ship inference without re-architecting every release.

Signed TFLite Micro bundles

Quantized weights, operator manifests, and checksums promoted through CI.

Firmware integration layer

C/C++ inference API wired into your RTOS tasks and interrupt priorities.

On-board benchmark harness

Repeatable latency and RAM profiling on target boards — not desktop-only tensors.

OTA model slot design

Dual-bank or A/B slots with rollback when a promoted model fails field gates.

Sensor preprocessing libraries

Shared DSP and normalization pipelines reused across product SKUs.

Handoff runbooks

Architecture diagrams and integration guides your embedded team can maintain.

01 — Problem

Why Embedded ML Is Hard

FieldSense had a model that worked in the lab but failed on the factory floor: too much RAM, unpredictable latency, and no path from Jupyter to the firmware release train.

"Our cloud model was accurate — but it never fit the MCU, and firmware could not host inference without missing real-time control deadlines."

Cloud-trained models that never fit MCU memory or power budgets
No toolchain to move from Jupyter notebooks to production firmware
Sensor data processed in the cloud — too slow for closed-loop control
Firmware and ML teams working in silos with no shared delivery model
OTA paths designed as an afterthought — blocking security review

Hardware Target

STM32 class MCU, 128 KB RAM budget, vibration sensor front-end.

Stakeholders

Firmware, ML, operations, and field service aligned on latency and accuracy SLAs.

Timeline

12-week delivery: 4 weeks prototype, 4 weeks optimize, 4 weeks fleet pilot.

Deliverables

Quantized model, firmware integration, OTA bundle, benchmark report, runbooks.

02 — Strategy

Our Embedded ML Approach

Constraint-first delivery: document silicon limits, validate on target boards early, then integrate inference loops with deterministic scheduling and OTA-ready model slots.

Hardware audit

Memory, power, sensors, and connectivity constraints documented on real boards.

Model & graph design

Architecture selected for target silicon and FieldSense accuracy SLA.

On-device validation

Benchmarks on STM32 with factory-representative vibration data.

03 — Stack

Edge & Embedded Toolkit

Toolchains we use to train, quantize, profile, and deploy lightweight ML on MCUs and embedded SoCs.

On-Device ML Runtimes

TensorFlow Lite Micro, ONNX Runtime, CMSIS-NN, and PyTorch export paths sized for MCU flash and SRAM. Applied to Lightweight ML for Embedded Systems engagements.

Tensorflow

ONNX

Tensorflow

Pytorch

ARM

Embedded RTOS & MCU

FreeRTOS, Zephyr, STM32, and ESP32 firmware integration with deterministic inference scheduling. Applied to Lightweight ML for Embedded Systems engagements.

Arduino

ESP32

ARM

Python

ARM

Linux

Edge Compute & Vision

Jetson, Coral, OpenCV, and GPU-class pipelines for perception workloads at the edge. Applied to Lightweight ML for Embedded Systems engagements.

MQTT

NVIDIA

OpenCV

Docker

Python

Tensorflow

Fleet OTA & Observability

MQTT telemetry, Grafana dashboards, Prometheus metrics, and CI/CD for model promotion. Applied to Lightweight ML for Embedded Systems engagements.

Docker

Python

Github

InfluxDB

MQTT

Grafana

04 — Process

Embedded ML Delivery Process

Hardware audit → on-device prototype → optimize → firmware integration → fleet deploy — with benchmark gates at every stage on real silicon.

Discover

Constraints, SLAs, and success metrics on representative hardware.

Prototype

Proof inference on target silicon with field-representative data.

Optimize

Quantize, prune, and profile until latency and power targets are met.

Integrate

Firmware APIs, sensor pipelines, and observability wired into the product.

Deploy

OTA rollout, fleet monitoring, and staged production cutover.

Care

Drift monitoring, retraining hooks, and optional retainer support.

Tools Used: TFLite MicroSTM32CubeEdge ImpulseGrafanaGitHub Actions

05 — Milestones

Project Snapshots

Visual milestones across a typical lightweight ML engagement — from board bring-up through field validation.

Claim your free consultation

06 — Delivery

Embedded ML Deliverables

Signed model bundles, firmware integration, benchmark reports, and OTA playbooks delivered for FieldSense production hardware.

Quantized model, RTOS integration, and OTA bundle in production

07 — In Field

Edge Inference In Production

How FieldSense devices run on-device vibration classification — fleet telemetry, model version tracking, and OTA status in the field.

Factory Floor Fleet Dashboard OTA Rollout

worksprout.us/portfolio

Live

FieldSense Edge Fleet

STM32 · TFLite Micro · OTA model slots · Sub-15 ms inference · Fleet dashboard

View portfolio

DeliveredQ4 2025

Duration12 Weeks

ServiceLightweight ML

Devices120+

Latency<15ms

Satisfaction100%

08 — Impact

Results from Embedded ML Work

Within 90 days of production cutover, FieldSense reduced false alerts, cut cloud inference costs, and met latency SLAs on every deployed node.

<15ms On-Device Inference

Sub-15 ms classification on STM32 with factory vibration profiles.

4× Model Size Reduction

INT8 quantization and operator fusion cut flash footprint for OTA.

128KB RAM Budget Met

Inference and preprocessing fit within FieldSense hardware constraints.

Key outcome: On-device inference eliminated round-trip cloud latency for closed-loop alerts — maintenance teams received vibration anomalies in under 15 ms while OTA kept models current across the fleet.

09 — Docs

Architecture & Engineering Visuals

Diagrams, benchmark charts, and integration artefacts produced during the FieldSense embedded ML engagement.

10 — Client Voice

Client Testimonial

★★★★★

"WorkSprout took our vibration model from notebook to STM32 firmware — with quantized weights, deterministic inference loops, and OTA slots our embedded team could own. We finally shipped edge ML that survives factory noise, power limits, and fleet rollout."

11 — Workflow