Threads or Processes? Choosing the Right Path in Embedded Systems

By [Employee Name]/[Designation] at EurthTech

Published on July 10, 2025

The Real-World Problem

Let’s say you're developing a smart fitness band—an example of modern embedded systems development and IoT product engineering.

It needs to:

• Continuously read accelerometer + heart rate data

• Push updates via BLE

• Display information on an OLED

• Stay alive for a week

• And run TinyML gesture detection using Edge AI in embedded devices

All on something like an Apollo4 Blue or STM32L4.

Here’s where you face the classic embedded decision point:Should this system use multithreading or multiprocessing?

Multithreading – The Speed and Simplicity of Sharing

For ultra-low-power MCUs used in wearables, FreeRTOS multithreading is ideal:

• Threads share memory

• Context switching is fast

• RAM usage is minimal

• Perfect for devices under 256 KB RAM

You divide the band’s logic like this:

Thread	Task
Thread 1	Sample sensor data every 10 ms
Thread 2	Run simple ML filter for gestures
Thread 3	Send BLE notification
Thread 4	Manage display and UI
Thread 5	Enter deep sleep if idle

Each thread uses FreeRTOS queues and semaphores to communicate.

Result? You get instantaneous response, smooth BLE packets, and ultra-low power. All on a single-core Cortex-M4 at 1.8V.

But there’s a catch.

When One Thread Brings the Whole System Down

One day, you push a firmware update. Your new sensor thread forgets to release a mutex, locking shared memory.

Suddenly—BLE crashes, the display hangs, and you can't send OTA fixes.

Why? Because in multithreading, every thread shares the same memory space. One misbehaving thread can corrupt others.

In embedded design, "no fault isolation" is dangerous—especially when your device is already in the user's hand.

That’s when your team suggests:

What if we isolate high - risk tasks into their own process ?

Multiprocessing – The Power of Separation

Now shift your perspective.

Imagine you're building an industrial IoT gateway, smart pole controller, or a smart-city edge node for:

• Vibration analytics

• Predictive maintenance using AI and IoT

• Secure cloud sync

• Camera/vision-based analytics

• Utility monitoring
  

This system runs on an SoC like i.MX8M or AM62x.

Here, multiprocessing (Linux) becomes the correct architecture.

Process	Task	Core
Process A	Sensor data handler	Core 1
Process B	ML model inference	Core 2
Process C	Secure cloud sync	Core 3
Process D	OTA update + watchdog	Core 4

Now, even if Process B fails (e.g., due to a corrupted TensorFlow Lite model), the gateway keeps transmitting data and doesn't reboot. You’ve built resilience into the architecture.

 A Hybrid Future – The Best of Both Worlds

Modern SoCs like STM32MP1 and i.MX RT1170 combine:

• Cortex-A (Linux + multiprocessing)

• Cortex-M (RTOS + multithreading)

This enables end-to-end embedded product design for:

• Smart poles with AI integration

• Computer vision for smart city surveillance

• GeoAI-based analytics

• AI-powered smart infrastructure

• Smart lighting + GIS mapping for utilities

  
  

Example: Smart AR Glasses

• Cortex-M7: sensor fusion, power control (RTOS threads)

• Cortex-A7: SLAM, camera processing (processes)

This hybrid architecture powers modern AI-enabled geospatial analytics and AI engineering solutions used in cities and industry.

 Multithreading vs Multiprocessing – Embedded SoC Cheat Sheet

Feature	Multithreading (RTOS)	Multiprocessing (Linux)
Memory Use	Very Low	High
Power Use	Very Low	Medium–High
Fault Isolation	Shared Memory	Full Separation
Core Use	Single-Core MCUs	Multi-Core SoCs
Determinism	Real-Time	Depends on OS tuning
IPC	Fast (queues/semaphores)	Complex (sockets/shared memory)
Typical Use	Wearables, sensors, BLE

 EurthTech Insights: When to Use What?

Choose Multithreading if:

• Device is MCU-based (STM32, Apollo4, nRF52)

• Ultra-low power is crucial

• Real-time behavior is required

• You’re building wearables, BLE nodes, or embedded AI India-focused IoT devices

Choose Multiprocessing if:

• You're building industrial gateways, smart poles, edge servers

• Need application isolation

• Running AI inference, computer vision, OTA, GIS analytics

• Working with Linux-based SoCs

  
  

Choose Hybrid if:

• You need real-time + AI

• Designing for smart city solutions provider use-cases

• Combining A-core Linux and M-core RTOS

 The Road Ahead

The world of concurrency is evolving:

• Rust improves thread safety

• Real-time Linux closes the determinism gap

• Hypervisors isolate RTOS + Linux workloads

• RISC-V brings thread-per-core architectures

• Digital twin smart city models rely on hybrid compute

Concurrency is becoming the infrastructure behind AI for smart cities and AI-powered embedded systems.

Final Thoughts — Build Embedded Systems Like You Tell a Story

Every product has characters (threads, processes), plot twists (crashes), and a climax (scalability and safety).

Your architecture should ensure:

• Stability

• Determinism

• Recovery

• Long-term reliability
  

At EurthTech, we help companies build everything from low-power IoT sensors to smart city infrastructure controllers, combining:

• Embedded systems development

• IoT & embedded services India

• Custom embedded software development

• AI product engineering company India expertise

• Geospatial and smart pole integration

We engineer products that scale — reliably, intelligently, and efficiently.

Need expert guidance for your next engineering challenge?

Connect with us today — we offer a complimentary first consultation to help you move forward with clarity.

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