FPGA vs GPU vs CPU vs MCU – hardware options for AI applications
Field-programmable gate arrays (FPGAs) offer numerous advantages for artificial intelligence (AI) applications. How do graphics processing units (GPUs) and traditional central processing units (CPUs) compare?
The term artificial intelligence (AI) refers to non-human, machine intelligence capable of making decisions in a manner similar to human beings. This includes the faculties of judgment, contemplation, adaptability, and intention.
A new UN Trade and Development (UNCTAD) report projects the global AI market will soar from $189 billion in 2023 to $4.8 trillion by 2033 – a 25-fold increase in just a decade. By 2030, Global GDP is expected to be 14% higher as a result of AI, with China and North America projected to see the greatest economic gains, according to a report released by the consultancy firm PwC.
The overall AI market encompasses a diverse range of applications, including natural language processing (NLP), robotic process automation, machine learning, and machine vision. AI is rapidly gaining adoption in various industry verticals and is driving the next significant technological shift, much like the advent of the personal computer and the smartphone.
The beginnings of artificial intelligence (AI) and its terminology can be credited to the Logic Theorist program created by researchers Allen Newell, Cliff Shaw and Herbert Simon in 1956. The Logic Theorist program was designed to emulate the problem solving skills of human beings and was funded by the Research and Development (RAND) Corporation. Logic Theorist is considered to be the first AI program and was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI), Dartmouth College, New Hampshire, in 1956.
Artificial Intelligence (AI) has transitioned from a buzzword to a fundamental utility. Whether it is Generative AI creating content, computer vision inspecting factory lines, or autonomous vehicles navigating traffic, the software is only as capable as the hardware running it.
While AI relies on algorithms, hardware is the bottleneck. The challenge for engineers today is not just "can this chip run AI?" but "can it run it efficiently, with the right latency, and within the power budget?"
The three traditional contenders, FPGAs, GPUs, and CPUs, have evolved, and a fourth category, the NPU, has entered the mainstream. Here is how they stack up for modern AI applications.
FPGAs
The Low-Latency, Sensor-Rich Specialist
Field-programmable gate arrays (FPGAs) are types of integrated circuits featuring programmable hardware fabric. This differs from graphics processing units (GPUs) and central processing units (CPUs) in that the function circuitry inside an FPGA processor is not hard etched. This enables an FPGA processor to be programmed and updated as needed. This also gives designers the ability to build a neural network from scratch and structure the FPGA to best meet their needs.
The reprogrammable, reconfigurable architecture of FPGAs provides key benefits to the ever-evolving AI landscape, enabling designers to quickly test new and updated algorithms. This delivers strong competitive advantages by speeding up time to market and achieving cost savings, as it eliminates the need for the development and release of new hardware.
FPGAs deliver a combination of speed, programmability and flexibility that translates into performance efficiencies by reducing the cost and complexities inherent in the development of application-specific integrated circuits (ASICs).
Key advantages FPGAs deliver include:
- Excellent performance with reduced latency advantages: FPGAs provide low latency as well as deterministic latency (DL). DL as a model will continuously produce the same output from an initial state or given starting condition. The DL provides a known response time, which is critical for many applications with hard deadlines. This enables the faster execution of real-time applications, such as speech recognition, video streaming, and motion recognition.
- Cost effectiveness: FPGAs can be reprogrammed after manufacturing for different data types and capabilities, delivering real value over having to replace the application with new hardware. By integrating additional capabilities — like an image processing pipeline — onto the same chip, designers can reduce costs and save board space by using the FPGA for more than just AI. The long product lifecycle of FPGAs can deliver increased utility for an application that can be measured in years or even decades. This characteristic makes them ideal for use in industrial, aerospace, defence, medical and transportation markets.
- Energy efficiency: FPGAs give designers the ability to fine-tune the hardware to match application needs. Utilising development tools like INT8 quantisation is a successful method for optimising machine learning frameworks like TensorFlow and PyTorch. INT8 quantisation also delivers favourable results for hardware toolchains like NVIDIA® TensorRT and Xilinx® DNNDK. This is because INT8 uses 8-bit integers instead of floating-point numbers and integer math instead of floating-point math. Proper utilisation of INT8 can reduce both memory and computing requirements, which can shrink memory and bandwidth usage by as much as 75%. This can prove critical in meeting power efficiency requirements in demanding applications.
FPGAs can host multiple functions in parallel and can even assign parts of the chip for specific functions, which greatly enhances operational and energy efficiency. The unique architecture of FPGAs places small amounts of distributed memory into the fabric, bringing it closer to the processing. This reduces latency and, more importantly, can reduce power consumption compared to a GPU design.
Key takeaway from Avnet Silica's AI expert, Michael Uyttersprot
Field Programmable Gate Arrays (FPGAs) have evolved into "Adaptive SoCs." Unlike other chips, where the data path is fixed, an FPGA allows you to rewire the hardware circuit itself to match your specific AI model.
They're best for:
- Robotics & Industrial Automation: Where data comes directly from sensors (cameras, LiDAR) and needs immediate processing.
- Mission-Critical Edge: Applications requiring deterministic latency (guaranteed response times), such as autonomous braking systems or surgical robotics.
- Legacy Interfacing: Connecting modern AI models to older, custom industrial connectors.
Summary
The advantage? FPGAs offer "streaming" architecture. While a GPU or CPU must wait for data to fill a memory buffer before processing (batching), an FPGA can process data pixel by pixel as it arrives. This results in the lowest possible latency. Modern devices (like the AMD Versal™ series) now include "hardened" AI engines alongside the programmable logic, offering the best of both worlds: high-performance math and flexible I/O.
The trade-off? They require specialised knowledge to program (VHDL/Verilog), though newer tools (like Vitis AI) allow software developers to target FPGAs using C++ or Python.
GPUs
The Heavyweight Champion of Training and Throughput
Graphic processing units (GPUs) were originally developed for use in generating computer graphics, virtual reality training environments, and video that rely on advanced computations and floating-point capabilities for drawing geometric objects, lighting, and colour depth. In order for artificial intelligence to be successful, it requires a substantial amount of data to analyse and learn from. This requires substantial computing power to execute the AI algorithms and shift large amounts of data. GPUs can perform these operations because they are specifically designed to quickly process large amounts of data used in rendering video and graphics. Their strong computational abilities have enabled them to become popular in machine learning and artificial intelligence applications.
GPUs are well-suited for parallel processing, which involves the computation of a large number of arithmetic operations simultaneously. This delivers respectable acceleration in applications with repetitive workloads that are performed repeatedly in rapid succession. Pricing on GPUs can be more competitive than other solutions, with the average graphics card having a five-year lifecycle.
AI on GPUs does have its limitations. GPUs generally do not deliver as much performance as ASIC designs, which are specifically designed for an AI application. GPUs offer a significant amount of computational power, albeit at the expense of energy efficiency and heat. Heat can create durability issues for the application, impair performance and limit types of operational environments. The ability to update AI algorithms and add new capabilities is also not comparable to FPGA processors.
Key takeaway from Avnet Silica's AI expert, Michael Uyttersprot
Originally designed for gaming, GPUs are the engine behind the current Generative AI boom. Their architecture consists of thousands of small cores designed to perform parallel math operations simultaneously.
They're best for:
- Model Training: Virtually all AI models are "taught" on massive clusters of GPUs in the cloud.
- Cloud Inference: Running massive large language models (LLMs) such as GPT-5, Google Gemini series, Anthropic Claude Opus 4.5 or Mixtral, to serve thousands of users concurrently.
- High-End Edge: Autonomous vehicles that need to process huge amounts of video data regardless of power consumption.
Summary
The advantage? Raw Throughput. When you need to process massive datasets or generate tokens at high speed, the GPU is unmatched. The software ecosystem (e.g., NVIDIA CUDA, ROCm) is also the most mature, making it easy to port models from research to reality.
The trade-off? Power and Heat. GPUs are energy-intensive. For battery-powered or fanless industrial devices, a high-performance GPU is often too hot and power-hungry to be a viable option.
CPUs + the NPU
The "AI PC" and Efficient Edge
The central processing unit (CPU) is the standard processor used in many devices. Compared to FPGAs and GPUs, the architecture of CPUs has a limited number of cores optimised for sequential serial processing. Arm® processors can be an exception to this because of their robust implementation of Single Instruction Multiple Data (SIMD) architecture, which allows for simultaneous operation on multiple data points, but their performance is still not comparable to GPUs or FPGAs.
The limited number of cores diminishes the effectiveness of a CPU processor to process the large amounts of data in parallel needed to properly run an AI algorithm. The architecture of FPGAs and GPUs is designed with the intensive parallel processing capabilities required for handling multiple tasks quickly and simultaneously. FPGA and GPU processors can execute an AI algorithm much more quickly than a CPU. This means that an AI application or neural network will learn and react several times faster on an FPGA or GPU compared to a CPU.
CPUs do offer some initial pricing advantages. When training small neural networks with a limited dataset, a CPU can be used, but the trade-off will be time. The CPU-based system will run much more slowly than an FPGA or GPU-based system. Another benefit of the CPU-based application will be power consumption. Compared to a GPU configuration, the CPU will deliver better energy efficiency.
Key takeaway from Avnet Silica's AI expert, Michael Uyttersprot
The CPU is the general-purpose brain of any computer. However, the modern CPU has undergone a radical shift. The latest processors (from AMD, Intel, Qualcomm, and Apple) now integrate a specialised block called a Neural Processing Unit (NPU).
They're best for:
- Local Inference: Running AI assistants, background blur, or noise cancellation on a laptop or embedded device.
- Small Language Models (SLMs): Running compact language models such as Llama 3 8B, Google Gemma 3 or Mistral 7B, locally for privacy and efficiency.
- Burst Workloads: Tasks that don't justify turning on a power-hungry GPU.
Summary
The advantage? Availability & Efficiency. If you have a modern PC or embedded board, you likely already have this hardware. The NPU is designed to run sustained AI workloads at very low power (milliwatts rather than hundreds of watts), keeping the main CPU free for other logic and preserving battery life.
The Trade-off? They lack the massive parallel horsepower of a discrete GPU. You wouldn't use a CPU/NPU to train a large model, but they are excellent for running them.
Tiny machine learning (TinyML) and Microcontrollers (MCUs)
AI on a Coin Battery
Seen as the next evolutionary phase of AI development, TinyML is experiencing strong growth. AI applications operating on FPGA, GPU and CPU processors are very powerful, but they can’t be used in all contexts like cellphones, drones and wearable applications.
With the widespread adoption of connected devices, there is a need for local data analysis that reduces dependency on the cloud for complete functionality. TinyML enables low-latency, low-power and low-bandwidth inference models on edge devices operating on microcontrollers.
The average consumer CPU will draw between 65 to 85 watts of power, while the average GPU consumes anywhere between 200 to 500 watts. In comparison, a typical microcontroller draws power in the order of milliwatts or microwatts, which is a thousand times less power consumption. This energy efficiency enables the TinyML devices to run on battery power for weeks, months and even years, while running ML applications on the edge.
TinyML, with its support for frameworks such as TensorFlow Lite, uTensor, and Arm’s CMSIS-NN, brings together AI and small, connected devices.
Benefits of TinyML include:
- Energy efficiency: Microcontrollers consume very little power, which delivers benefits in remote installations and mobile devices.
- Low latency: By processing data locally at the edge, data doesn't need to be transmitted to the cloud for inference. This greatly reduces device latency.
- Privacy: Data can be stored locally, not on cloud servers.
- Reduced bandwidth: With decreased dependency on the cloud for inference, bandwidth concerns are minimized.
The future of TinyML using MCUs is promising for small edge devices and modest applications where an FPGA, GPU or CPU are not viable options.
Key takeaway from Avnet Silica's AI expert, Michael Uyttersprot
Not every AI application requires a Linux operating system. "TinyML" brings machine learning to microcontrollers (like the Arm Cortex-M series).
They're best for:
- Wearables: Smartwatches and fitness trackers.
- Predictive Maintenance: Vibration sensors on motors that detect faults before they happen.
- Smart Home: Voice command recognition (keyword spotting) in light switches.
Summary
The advantage? Extreme Efficiency. These devices operate in milliwatts or microwatts. They can run simple AI models on a coin-cell battery for years.
Key takeaways
- Stop looking for one "best" chip. The market has fragmented. The "best" chip depends entirely on whether you are training a model (GPU) or running it (FPGA/NPU).
- Latency vs. Throughput: If you need to process 1,000 video streams at once, choose a GPU. If you need to make a decision based on one video stream in 1 millisecond to prevent a robot from crashing, choose an FPGA.
- The Rise of the NPU: For consumer and general embedded applications, the CPU+NPU combination is becoming the new standard, balancing performance with energy efficiency.
About Avnet Silica
Choosing the right technology partner is critical to navigating these options. Whether you need the raw power of a GPU or the custom precision of an FPGA, Avnet Silica can connect you with global partners to help you benchmark and select the right silicon for your innovation.
With a century of innovation at our foundation, Avnet Silica can guide you through the challenges of developing and delivering new technology at any — or every — stage of your journey. We have the expertise to support your innovation, turn your challenges into opportunities and build the right solutions for your success. Make your vision a reality and reach further with Avnet Silica as your trusted EMEA technology partner.
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Understanding the unique opportunities and hardware challenges across different deployment tiers is essential for designing impactful AI systems that meet the demands of modern applications.
