When To Use FPGA vs Microcontroller

Author: Ken Obuszewski, Product Management, VORAGO Technologies
Feb 20, 2024

FPGAs and Microcontrollers have long histories of deployment across a broad set of embedded system markets including automotive, the internet of things (IoT), and aerospace and defense, and often represent one of the key strategic choices for a customer’s product design. They have unique capabilities which influence the decision to choose an FPGA or an MCU, and in some scenarios to deploy both in a design. This article is intended to provide insights on making cost-efficient, informed decisions for your projects.

What is an FPGA and What Does it Mean?

A Field Programmable Gate Array (FPGA) is a highly configurable digital integrated circuit, allowing the user to create custom logic designs. FPGAs are programmed using Hardware Description Languages, which requires a somewhat unique skillset. They are also a valuable tool for workloads that benefit from large scale parallel processing or parallel computing. FPGAs vary widely in terms of functionality, price, and power consumption. In addition to parallel computing use cases, engineers are likely to choose an FPGA as an alternative to building a custom application specific integrated circuit (ASIC), with the greatly reduced R&D investment offsetting a higher average unit selling price (ASP).

What are Common FPGA Applications and Examples

FPGAs were initially developed in the mid-1980s as an alternative to custom ASICs. Common use cases included:

• Pre-silicon prototyping

• Evolving and multi-standard support

• Early neural networks

• Digital Signal Processing (DSP) applications

The telecom and networking markets helped accelerate the growth of the FPGA market in the early 1990s, and today FPGAs can be found across all major industry verticals. Examples include image processing and signal processing of medical imaging data and signal processing and custom algorithm implementation in industrial control applications.

The modern data center has become a leading application for today’s FPGA, providing lower power implementations than general purpose GPU and advanced CPU solutions. Target workloads include AI/ML, security and encryption, and real-time analytics (i.e., financial trading). Modern FPGA vs microcontroller comparisons highlight that FPGAs excel in applications requiring high-speed parallel processing, such as real-time analytics and encryption.

The Aerospace and Defense market has been a user of FPGAs from the early days when field re-programmability was critical due to long deployment timeframes. The need for field programmability was especially critical in the space applications market, which has continued to evolve use cases for FPGAs, including:

• Onboard edge computing microprocessor technology, including signal and image processing, data compression and encryption to optimize data transmission from satellites

• Software defined radio

• Navigation and guidance systems

• AI and neural networking acceleration

Please refer to the FPGA in Space Applications blog for additional details.

Understanding FPGA Design and Architecture

• FPGAs are complex integrated circuits, with multiple subsystems described as follows:

Logic functions including:

• LUTs (Look-Up Tables): Programmable combinational logic gates and elements

• Registers: Intermediate value storage and synchronization of tasks

Digital Signal Processing (DSP) blocks

• Fast Fourier transforms (FFT), multipliers, and finite impulse response filters (FIR)

Memory

• Typically, SRAM

I/O

• Interface to external devices

Hard IP

• Occasionally industry standard IP such as Arm® CPU or GPUs are integrated to create FPGA SoCs

What are (MCU) Microcontrollers?

Microcontrollers are embedded integrated circuits which contain a CPU core, embedded memory, input/output (I/O) ports, and application specific peripherals. MCUs are optimized towards low cost and low power consumption, focused on specific applications and workloads.  Historically, the CPU core in an MCU was a custom architecture built by the semiconductor vendor, with more recent momentum towards industry standard architectures such as Arm®.  Microcontrollers are configurable using software and well-known programming languages such as C. As a result, there is a large community available to develop MCU powered applications.

Is an MCU a Microprocessor

Compared to an MCU, a microprocessor (MPU) is a higher performance and a more general-purpose semiconductor device. The distinction between an MCU and a microprocessor (MPU) begins with the embedded memory architecture, where an MCU primarily relies on an embedded system of memory (volatile and non-volatile), and an MPU relies on external memory (i.e., DRAM and Flash) in addition to internal memory. This allows an MPU to run a higher performance operating system, such as Linux, and to perform advanced computing tasks including artificial intelligence, digital signal processing and general computing tasks. In recent years there has been a focus on providing a continuum between MCU and MPU. This includes running Linux on the MCU and a Real-time Operating System (RTOS) on an MPU.

What are Common Microcontroller Applications and Examples

A key industry that has driven innovation in microcontrollers is the automotive market, where a premium has been placed on developing application specific MCUs optimized for cost and power consumption. Examples include engine control, airbag systems, and electronic braking systems. MCUs have been deployed across every industry, such as industrial programmable logic controllers (PLCs) and motor control systems, portable consumer devices, smart meters, medical devices, and of course aerospace and defense systems. 

In Space applications, MCUs have become critical components, largely due to the existence of radiation hardened solutions such as VORAGO Arm-based rad hard MCUs. Because of co-existence with components that are not radiation hardened a key use case for rad hard MCUs is intelligent watchdog which maintains the radiation resiliency of the system. Specific protections include: 1) Single event latch-up detection (SEL) and the ability to reset the system components and even reprogram an FPGA.  An MCU can adjust the in-orbit system operation to avoid SEL; 2) Scanning for single event functional interrupts (SEFIs); 3) Memory scrubbing correcting any corrupted bits and writing the corrected data back to memory.

Additional MCU use cases include motor control, guidance, navigation, and control (GNC) systems for spacecraft, onboard processing of telemetry data, and payload systems. 

Understanding Microcontroller Design and Architecture

Here is an in-depth look at microcontroller architecture components:

Central Processing Unit (CPU)

• The CPU is the brains of an MCU and represents a critical architectural point for both the semiconductor provider and the end customer

• The most prevalent CPUs in the Space market include Arm® and the emerging RISC-V architecture

• CPUs for microcontrollers have evolved from 8- and 16-bit architectures and are primarily 32-bit deployments today

Memory

• Program memory: Non-volatile memory, typically flash memory or read-only memory (ROM)

• Data memory: Volatile memory, typically static random-access memory (SRAM)

• Error correction code (ECC) is a key feature in MCUs that allows for mitigation measures from radiation exposure

• MCUs also may have external memory expansion options

Input/Output (I/O) Ports and Peripherals

• General-Purpose I/O (GPIO): Can be configured as inputs or outputs to connect to the external world

• Analog Peripherals

  o   Analog to Digital Converters (ADC): Converts analog signals into digital values

  o   Digital to Analog Converters (DAC): Converts digital signals into analog voltages

  o   Analog Comparators: Compares two analog voltages and outputs a digital value

• o   Communication Interfaces

  o   Universal Asynchronous Receiver-Transmitter (UART)

  o   Serial Peripheral Interface (SPI)

  o   Inter-Integrated Circuit (I2C)

  o   Controller Area Network (CAN)

  o   Universal Serial Bus (USB)

  o   Ethernet

  o   SpaceWire

FPGA vs MCU – Understanding the Differences

Let’s summarize the major differences between a microcontroller vs FPGA:

Cost vs Flexibility:

• In almost all cases an MCU solution will be lower cost than an FPGA. An FPGA is designed to provide optimal flexibility for hardware functionality, where an MCU provides a balanced approach to general purpose vs application specific capabilities.

• The flexibility of an FPGA is primarily achieved via the inherent design of an FPGA to allow hardware re-programmability. MCU flexibility is achieved primarily from software and a limited number of hardware configuration options.

Power Consumption:

• A key tenant of an MCU is low power consumption and the IP (i.e., Arm® CPU) and design methodologies are heavily focused on low power consumption. MCU proliferation in battery powered devices has led to best-in-class power profiles.

• FPGAs have been able to leverage advancements in semiconductor design to improve power consumption; however, the focus on higher performance parallel processing applications and larger designs to accommodate flexibility they cannot match the MCU power consumption.

Development Complexity

• Outside of hardware PCB design, the skill sets of MCU vs FPGA developers are in many cases very different. 

• Most development cycles for an MCU are focused on software development, either assembly language or more often using high-level languages such as C and C++. For lower end MCUs, much of the effort is focused on code optimization to ensure fitting within potential memory footprint constraints. For higher end MCUs, knowledge of an operating system such as Linux or VxWorks may be required.

• The skillset of an FPGA programmer is more akin to that of a semiconductor design flow, with knowledge of Hardware Description Languages (HDLs) like Verilog or VHDL. This may also lead to more complex debug environments.

FPGA vs MCU: Main Similarities

Let’s summarize the major similarities between an FPGA vs microcontroller:

Embedded System Focus:
Both FPGAs and microcontrollers are designed for use in embedded systems, where they are integral components in applications that require compact, efficient processing of data in real-time. Whether it’s a connected IoT device or an automotive control system, both FPGAs and MCUs are designed to carry out specific tasks with minimal power consumption and high reliability.

Low Power Operation:
Power efficiency is a key design aspect for both technologies. FPGAs have become increasingly power-efficient over time, especially in applications that demand low-speed, low-complexity processing. Similarly, microcontrollers are explicitly optimized for low power consumption, making them ideal for battery-operated applications where power management is crucial.

Real-Time Data Processing:
FPGAs and MCUs are both adept at real-time data processing in applications where precise timing and fast responses are critical. An MCU might handle time-sensitive tasks like motor control, while an FPGA could be responsible for high-speed data encryption. In both cases, these devices excel in handling tasks that require immediate and consistent action.

Integration into Multi-Component Systems:
In modern embedded designs, FPGAs and MCUs are frequently used together in multi-component systems. For instance, an MCU might handle general computing tasks, while an FPGA is used to accelerate specific functions or offload more complex processing. Their combination enhances the overall system performance, providing the best of both worlds.

By recognizing the similarities between FPGAs and microcontrollers, designers and engineers can make more informed decisions about which technology is best suited for their application, or whether integrating both is the optimal solution for the task at hand.

Industries That Prefer Microcontroller vs FPGA

Choosing between a microcontroller and an FPGA depends largely on the industry and specific application needs. While field programmable gate arrays (FPGAs) offer high customization and parallel processing, microcontrollers (MCUs) excel in cost-effectiveness and ease of development.

Industries That Prefer Microcontrollers (MCUs)

Microcontrollers are ideal for applications that require sequential processing and interact with external devices. They are commonly used in:

• Consumer Electronics – Devices like smart home systems, wearables, and remote controls benefit from microcontroller programming, as MCUs handle tasks efficiently with a fixed hardware structure.

• Automotive Systems – Many modern vehicles use microcontrollers for central processing unit (CPU) functions in engine control units (ECUs), airbag systems, and infotainment.

• Medical Devices – Low-power, battery-operated medical equipment like glucose meters and pacemakers rely on microcontrollers for reliable operation

• Industrial Automation – Factories use MCUs in programmable logic controllers (PLCs) for sensor control and robotic automation.

Industries That Prefer FPGAs

On the other hand, FPGAs vs microcontrollers are chosen for industries requiring high-speed, parallel data processing:

• Aerospace & Defense – Configurable logic blocks allow FPGAs to handle real-time signal processing for radar, encryption, and avionics.

• Telecommunications – Network infrastructure relies on FPGA vs MCU for handling high-bandwidth data streams and 5G base station

• AI & Machine Learning – Field programmable gate arrays accelerate AI workloads and deep learning models with parallel processing capabilities.

• Finance & High-Frequency Trading – The low-latency nature of FPGAs makes them ideal for real-time trading algorithms.

FPGA vs Microcontroller – How to Know Which One to Use

Now that we have articulated the distinction between an FPGA and an MCU, let’s discuss how to choose the right components for your design. This can be looked at in a few vectors:

• If the target application does not have clearly defined hardware specifications, and cost and power are not critical design parameters, an FPGA might be the right choice.

• In cases where there are clearly defined constraints in terms of cost, power consumption, and/or footprint, an MCU is the best choice. This also applies when the customer desires to deploy multiple variants of the same application with differing performance/cost targets. In this case, development costs can be optimized using different MCU variants from the same supplier/product family or even multiple instances of the same product.

• For high performance use cases where parallel processing is of value, then an FPGA might be the preferred choice. These workloads likely could also be solved with an MPU solution, especially if the customer would like to maintain consistency in hardware and software CPU architecture with MCU to MPU scalability.

• As previously stated, MCU and FPGA often co-exist, such as the example of using a radiation hardened MCU, such as the VORAGO VA41630, as a system watchdog and to provide field re-programming of an FPGA. It is also important for customers to be mindful of how they invest their R&D dollars as FPGAs require a more specialized skillset with unique toolchains. There is a larger community with the capability to support MCUs, both in terms of university training and a large third-party ecosystem of support. If cost, power efficiency, and ease of development are key, an MCU is the better option. For example, check out the M4 Microcontroller and M0 Microcontrollers for cost efficient, power optimized solutions.

Cost Considerations: FPGA vs MCU

When comparing FPGA vs microcontroller, cost is a critical factor that influences design decisions.

Microcontroller Cost Factors

• Lower Unit Cost: Due to their fixed hardware structure, MCUs are mass-produced and have a lower per-unit price than FPGAs.

• Simpler Development: Microcontroller programming requires standard toolchains, reducing engineering costs.

• Lower Power Consumption: MCUs are optimized for sequential processing, making them energy-efficient for battery-powered devices.

FPGA Cost Factors

• Higher Upfront Cost: The flexibility of field programmable gate arrays comes at a higher initial price due to configurable logic blocks and specialized development tools.

• Increased Power Usage: Compared to MCUs, FPGAs consume more power, especially when handling complex workloads.

• Scalability Challenges: Unlike MCUs, which use a central processing unit (CPU) to execute predefined tasks, FPGAs require custom hardware design, increasing time and development expenses.

FPGA vs MCU: Which One is More Cost-Effective?

For mass-produced products and cost-sensitive applications, microcontroller vs FPGA favors MCUs. However, in performance-critical industries like AI, networking, and aerospace, the long-term advantages of FPGA vs MCU often justify the higher cost.

FAQ’s for FPGA vs Microcontroller:

What is the difference between an FPGA and a Microcontroller?

• An FPGA is a highly configurable digital integrated circuit, allowing the user to create custom digital circuits, whereas a microcontroller is a more general-purpose application specific device that is primarily software configurable.

Can an FPGA replace a microcontroller?

• Sometimes – specifically in scenarios where cost and power consumption are not critical, and there is a need to implement custom hardware functionality.

Can FPGA and a Microcontroller work together?

• Yes, and in several cases, it makes sense to pair them together. The Space market is a prime example where a radiation hardened MCU, such as the VORAGO VA41630, is utilized as a system watchdog and to provide field re-programming of an FPGA.

Why do FPGA builds take so long?

• An FPGA workflow is similar to a semiconductor design flow, which requires specialized skills and is also dependent on the quality of the Hardware Description Language Tools (HDL). The complexity of the design may also require the optimization of multiple components and require multiple iterations.

Is FPGA better than Microcontrollers?

• As is always the case there is no “one size fits all” and the choice of using an MCU, an FPGA, or both requires an ROI analysis by the customer.  Both bring unique capabilities.

Want more information? Contact VORAGO: https://www.voragotech.com/contact-us