Student Akash Asbro has designed a Microchip ATmega328P-based development board with a difference: it has four cores running side by side, instead of the usual one.
“The Quad-Core AVR Microcontroller Board is a custom-designed embedded platform that integrates four ATmega328P chips to enable true hardware-level multitasking,” Asbro explains of his creation. “Designed for robotics, IoT [Internet of Things], and real-time automation systems, it features a modular architecture with a custom task allocation library that enables each core to handle dedicated tasks independently — bringing multi-core performance to the low-cost, low-power microcontroller space.”
The Quad-C brings something a little usual to AVR development: quad-core processing capabilities. (📷: Akash Asbro)
Multi-core processing is nothing new: application-class processors have offered multiple cores — and, before that, multiple physical processors on a single motherboard — for decades, and many modern microcontrollers do the same. Sometimes the cores are used truly independently, while other times they operate in “lockstep” — meaning they perform the same tasks at the same time, and if their results differ throw an error for applications where functional safety is imperative.
Asbro’s board doesn’t include a modern multi-core microcontroller, though, but four physical ATmega328P cores — capable, like classic multi-processor computers, of working together. “Quad-C achieves true parallelism by distributing tasks across independent processors,” Asbro says. “Each microcontroller can be assigned dedicated roles — such as sensor data acquisition, motor control, or wireless communication — ensuring better responsiveness and modular design.”
A custom library handles task distribution, with its creator promising “true parallelism” and dynamic task distribution. (📷: Akash Asbro)
“The Quad-C board relies on an exclusive custom task allocation library as its main system component,” Asbro continues. “The customized task allocation library enables microcontrollers to distribute tasks dynamically among each other which optimizes the processing resource utilization. Through the system developers have the ability to distribute individual tasks across MCUs which leads to both parallel processing and enhanced performance when handling real-time applications.”
The project is documented in full, with source code, schematics, and Gerber files, on Hackaday.io.
