MCUs or Microcontroller Units have been the norm for many embedded applications requiring specialized, efficient computing. But the industry is changing, and a simple MCU is not enough anymore. 

Hybrid MCUs are the new paradigm, letting even these compact systems handle a variety of tasks themselves. But what is an MCU, and what makes one hybrid? Why do we need hybrid MCUs? Let’s find out. 

What is a Microcontroller Unit (MCU)?  

The usual PC configuration involves slotting a microprocessor into a motherboard, and outfitting it with other components and peripherals to create a powerful system. The thing is, in many embedded scenarios, such a computer is overkill.  

Many scenarios require an extremely efficient system that uses minimal space, energy, and setup cost to achieve the task, which calls for microcontroller units, or MCUs. Sometimes baked into a single silicon, sometimes realized through a System-on-Chip (SoC) or even a Single-Board-Computer (SBC), such computers are a standard in low-power computing.  

But even these simple scenarios demand a lot more out of the computers these days, and the usual MCU is not able to meet the demands anymore. This is leading to the rise of hybrid microcontrollers, which combine components specialized in different types of tasks on the same board for flexibility. 

Why Hybrid?

Embedded computing is undergoing massive changes with the advent of AI and edge computing. Earlier, the framework favored cloud computing; the idea was to have bare-bones systems that would offload the heavy computing to the cloud and keep their own requirements low.

But due to the latency involved and the security risks of data transmission, it is now more favored to deploy systems that can handle their own compute on-site. AI applications have added even more need for powerful computing on edge systems, as the data collected through sensors can be analyzed right there. 

All these developments mean that the normal bare-bones MCU is no longer sufficient. Modern embedded systems require AI computing, network processing, and even image processing. While also needing to be efficient for normal computing tasks, which means that simply slapping the strongest processors isn’t the solution. 

How Hybrid Microcontroller Units Work

The premise of a hybrid MCU is straightforward – a single chip or a board combining multiple types of processors to handle different workloads efficiently. This means dedicated hardware for AI processing, and a separate CPU for normal tasks. They are integrated closely to rely on the same system memory and storage, creating an efficient design without sacrificing performance. 

Depending on the requirements, other specialized sub-systems may also be integrated to deal with signal processing or data transfer without invoking the CPU. The advantage of using this approach instead of a powerful general-purpose processor is twofold. One, it gives better response with lower latency as the tasks are distributed to the hardware specialized for it. Two, it keeps the power consumption low as the multiple chips are only utilized when needed, instead of drawing power all the time. 

These features are crucial in real-time applications where the system must process the data immediately but also be capable of performing basic computations throughout the day without overheating. A hybrid MCU gives a good middle ground between a basic MCU and a complete processor with graphics card setup, filling a very crucial niche in embedded computing.  

Are Hybrid MCUs Better Than Normal Processors?

It is important to understand that a microcontroller unit is not the best answer for every kind of problem. It is an embedded configuration that does well in certain conditions, while normal processors do better for other situations. 

MCUs are generally deployed in applications that require quick, real-time responses and efficiency, involving things like smart kiosks, IoT, etc. Hybrid MCUs are a great upgrade for these, adding AI capability and multitasking to these simple computers that normally don’t have the hardware for it. And they do this without drastically increasing their running costs or heat produced, keeping to the original aim of this approach.

That doesn’t mean that MCUs, hybrid or otherwise, can handle everything though. For many embedded applications, it is better to get a normal processor that can handle general computing a lot better while also having a discrete GPU for image processing or AI workloads. Newer Intel chips are also equipped with performance and efficiency cores by default, so they can balance their loads better even without an MCU.