FPGA and Machine Learning: Unlocking the Future of AI Hardware 

As machine learning (ML) and artificial intelligence (AI) continue to grow, hardware capable of efficiently accelerating these tasks is in demand. Traditionally dominated by GPUs, Field Programmable Gate Arrays (FPGAs) are gaining traction for their customizable, high-performance solutions that address the unique needs of AI applications. 

At Fidus, we leverage FPGAs to deliver power-efficient, custom solutions for AI tasks. Our partnerships with industry leaders such as AMD, Intel, and Lattice enable us to offer cutting-edge FPGA designs for various AI applications. 

Here’s what we cover in this blog post: 

The Role of FPGAs in Machine Learning 

FPGAs excel in machine learning applications that demand low-latency and task-specific customization, such as real-time AI inference. Their reconfigurable nature allows for optimized performance, making them ideal for applications where energy efficiency is critical, especially in edge computing and mobile AI tasks.

At Fidus, we understand that no two projects are the same. That’s why we leverage the reconfigurable architecture of FPGAs to customize hardware solutions that perfectly align with the unique requirements of each ML algorithm. This flexibility leads to several benefits: 

• Customization
• Low Latency
• Power Efficiency
• Reconfigurability for Future-Proofing
• Real-Time Performance
• Power Efficiency for Edge Applications
• Custom Hardware for Tailored Solutions

Customization

Unlike GPUs, which are designed for broad parallel processing, FPGAs can be tailored for specific tasks, such as neural network inference, allowing for optimized performance and efficiency. Our engineers excel in fine-tuning FPGA designs to maximize the potential of each application. 

Low Latency

FPGAs excel in applications requiring real-time processing, making them ideal for industries like autonomous driving, robotics, and high-frequency trading. Fidus’s experience in deploying low-latency solutions ensures that your AI applications perform at their best when milliseconds matter. 

Power Efficiency

By customizing the FPGA to perform only the necessary computations, it typically consumes less power than a GPU, especially in embedded software applications. This is particularly advantageous in edge computing and mobile devices, where energy conservation is critical.  

Reconfigurability for Future-Proofing 

FPGAs adapt through reprogramming to support new AI algorithms, frameworks, and application needs, offering adaptable processing for changing workloads. This eliminates hardware replacement cycles as technology progresses—your systems adapt with advances. The Fidus team develops FPGA solutions that provide sustained benefits, supporting your position in the AI technology curve.

Real-Time Performance 

Applications that demand microsecond precision, such as autonomous driving, robotics, and live video analytics, benefit from FPGA architecture. The processing minimizes latency for quick decisions, surpassing GPU capabilities in time-critical scenarios. Fidus applies deep FPGA design expertise to build AI systems that respond at the speed your applications demand.

Power Efficiency for Edge Applications 

Edge computing and mobile devices require precise power management. FPGAs use less power than GPUs by executing only essential computations for each task. This optimization increases battery duration in portable systems and reduces thermal management requirements in power-limited settings. Fidus creates FPGA designs that balance high performance with power conservation, making them effective for edge AI deployment.

Custom Hardware for Tailored Solutions 

AI projects present unique challenges, and FPGAs address these through customization. The architecture permits task-specific optimization, whether implementing neural network inference or processing high-bandwidth data flows. Fidus engineers develop FPGA designs matched to your specifications, optimizing AI system performance and efficiency for your use case.

Implementing AI on FPGAs: Steps and Tools 

Implementing AI on FPGAs can be complex, but with Fidus’s proven methodology and access to the latest tools, we streamline the process to deliver results efficiently. Here’s how we do it: 

Power Efficiency Techniques for FPGA-Based ML Deployments 

Achieving Superior Power Efficiency with FPGAs One of the standout advantages of FPGAs over GPUs in machine learning is their ability to achieve superior power efficiency. This is particularly important in edge computing environments, where energy resources are limited.  

Here are some key techniques used to optimize power efficiency in FPGA-based ML deployments: 

• Dynamic Voltage and Frequency Scaling (DVFS): By adjusting voltage and frequency according to workload demand, we reduce power consumption during less intensive operations, enhancing overall efficiency. 
• Clock Gating: Our designs incorporate clock gating to minimize power draw during idle periods, contributing to longer battery life in portable devices. 
• Resource Sharing: We implement efficient resource sharing strategies to reduce power consumption by reusing hardware resources across multiple tasks within the FPGA. 
• Adaptive Power Management: Fidus designs feature adaptive power management, dynamically adjusting power usage in response to real-time workload demands, ensuring that your systems are always operating at peak efficiency. 
• Power-Aware Synthesis: During synthesis, our team uses power-aware tools to optimize the placement and routing of logic, minimizing energy use without sacrificing performance. 

FPGA vs. GPU for Machine Learning: Which is better?

FPGAs offer advantages over GPUs in machine learning tasks that require low latency and energy efficiency, especially in real-time applications like autonomous systems. While GPUs excel in general-purpose tasks with high throughput, FPGAs are better suited for specific, optimized workloads where customizability and power efficiency are key.

Below is a detailed comparison of the two: 

Aspect	FPGA	GPU
Performance	Optimized for specific tasks with custom logic	High throughput for general-purpose tasks
Power Efficiency	More power-efficient, especially in edge applications	Higher power consumption under load
Latency	Ultra-low latency, ideal for real-time AI	Higher latency due to generalized design
Flexibility and Customizability	Fully customizable for task-specific optimization	Limited by fixed architecture
Development Complexity	Higher, requires specialized knowledge	Easier with mature development frameworks
Cost	Potentially lower for targeted applications	Higher, especially for high-performance models
Scalability	Scalable with significant design effort	Easily scalable with existing infrastructure
Ecosystem and Software Support	Growing, supported by tools from AMD and Intel	Extensive and well-established
Programming and Development Tools	Requires hardware-specific tools	Supported by mainstream ML frameworks
Throughput	High for specific, optimized tasks	High throughput for general-purpose workloads
Real-Time Processing Capabilities	Superior for real-time AI tasks	Good, but not as optimized as FPGAs
Deployment and Integration	Complex, requires custom integration	Easier with more off-the-shelf solutions
Hardware Availability	Increasing, particularly in specialized sectors	Widely available and adopted in AI/ML
Support for Specific ML Frameworks	Supported, but less extensive than GPUs	Broad support across major frameworks
Market Adoption and Industry Use Cases	Emerging, particularly in edge computing	Dominant in most AI/ML fields

FPGA vs. GPU: Which is Better for High Productivity Computing? 

Choosing the Right Hardware for High-Productivity ML Workloads The choice between FPGA and GPU for high-productivity computing in machine learning largely depends on the specific requirements of the task at hand. GPUs are typically favored for their sheer processing power and ease of use in general-purpose machine learning tasks. However, FPGAs offer significant advantages in scenarios where: 

• Real-Time Processing: FPGAs are unmatched in applications requiring immediate data processing. For tasks like autonomous vehicle control and real-time market analytics, FPGAs deliver the low-latency performance needed to make decisions in real time. 
• Energy Efficiency: In environments where power consumption is a critical factor, such as in remote sensing or mobile AI applications, FPGAs excel. Fidus designs FPGA-based solutions that maximize energy efficiency without compromising on performance. 
• Customizability: For ML tasks that benefit from hardware-level customization, FPGAs offer the flexibility to design and optimize the hardware specifically for your algorithms. This often results in superior efficiency and performance compared to more generalized hardware. 

Practical Applications: FPGAs in Machine Learning 

FPGA Accelerators for Machine Learning FPGAs are increasingly being adopted as accelerators for AI tasks, providing a unique blend of performance and efficiency. AMD Alveo™and Intel®Gaudi® are prime examples of FPGA accelerators designed to boost machine learning workloads by customizing the hardware to the specific needs of AI algorithms. These accelerators offer several advantages: 

• Custom Hardware Configurations: Fidus designs FPGA accelerators tailored to the specific needs of AI models, optimizing performance and efficiency. 
• Low Power Consumption: We specialize in creating FPGA solutions that are energy-efficient, making them ideal for applications where power is a critical concern. 
• Real-Time Processing: FPGAs excel in real-time AI tasks, such as autonomous systems and live data processing, where quick, accurate responses are crucial. 

FPGA Machine Learning Project Examples

FPGAs drive machine learning acceleration, offering solutions that combine efficiency, customization capabilities, and real-time processing performance. Here are three FPGA-powered machine learning projects that show their impact for Fidus customers:

• High-Speed Video Analytics for Real-Time Decision-Making
• Low-Power Edge AI for IoT Devices 
• Financial Market Prediction with Low-Latency AI

High-Speed Video Analytics for Real-Time Decision-Making 

Applications like factories, urban monitoring, and autonomous vehicles require instant decision-making. An FPGA-powered video analytics system detected anomalies and classified events in real time. The implementation used custom PCIe cards with parallel processing architecture to manage large-scale video datasets at minimal latency. The system processed 120,000 frames per second, enabling immediate responses for industrial automation and smart monitoring systems.

Low-Power Edge AI for IoT Devices 

Edge computing hardware like drones, smart cameras, and IoT sensors face strict power limitations. FPGAs strike an optimal balance between processing power and energy usage in these scenarios. Fidus built an FPGA implementation for drone-based object detection that reduced power consumption compared to GPU alternatives. This created a system that maintained consistent AI inference while maximizing battery duration—suited for remote operations in agriculture and emergency response missions.

Financial Market Prediction with Low-Latency AI 

High-frequency trading (HFT) requires AI systems that process large data volumes with immediate response times. An FPGA-based AI accelerator handled real-time market data streams. The system achieved faster performance than GPU implementations through reduced latency and predictable processing times, leading to quicker trade execution and increased returns. The Fidus design integrated with existing trading infrastructure to maximize speed and dependability.

Case Study: Fidus Develops Custom PCIe Card for AI Supercomputer 

Overview 

Fidus was approached to develop a cutting-edge custom supercomputer platform built specifically for AI machine learning with a focus on video data training. The project required a bespoke solution capable of handling large-scale video datasets for both training and inference workloads. Central to the system’s performance was the need for high-bandwidth and low-latency data transfer across the platform, essential for managing the demands of video training in machine learning models. 

Fidus was tasked with designing and developing a PCIe card that could meet these stringent requirements while ensuring seamless integration within the supercomputer platform. 

Customer 

A leading tech company specializing in AI-driven solutions and video data analytics. The company needed to build a supercomputer platform that could effectively process and train machine learning models using vast amounts of video data. This required an innovative approach to manage the large-scale data and ensure real-time processing and inference. 

Project Challenge 

The client needed a PCIe card that could manage vast amounts of video data while ensuring real-time processing and seamless integration within the supercomputer platform. The primary challenges included: 

• High-bandwidth data transfer for processing large video datasets. 
• Low-latency architecture for real-time AI training and inference. 
• Scalability to accommodate future system expansions. 

Fidus’s Solution 

Fidus designed a custom PCIe card optimized for AI machine learning, ensuring high-speed data transfer, low latency, and scalability: 

• Custom PCIe design to handle the heavy data loads of video training. 
• Low-latency architecture that enabled real-time processing. 
• Seamless integration with the existing supercomputer infrastructure, ensuring scalability for future upgrades. 

By tailoring the solution specifically to the customer’s machine learning needs, Fidus helped unlock the full potential of their AI models, accelerating their time to market and improving system performance across the board. 

Fidus was key to the success of our AI project. Their custom FPGA solution provided fast, low-latency data transfer, which made a huge difference in our machine learning performance. Their expertise and support were invaluable.

AI Project Lead, Tech Company

Conclusion 

FPGAs are quickly becoming a cornerstone of the AI hardware landscape, offering unmatched customization, efficiency, and real-time processing capabilities. At Fidus, we’re not just keeping up with these trends—we’re leading the way. With our deep expertise and partnerships with industry leaders like AMD and Intel, we’re ready to help you harness the full power of FPGAs for your AI projects. 

If you’re looking for a trusted partner to help you develop cutting-edge hardware solutions for AI and machine learning, Fidus has the expertise you need. Our ability to design custom hardware that integrates seamlessly into complex systems ensures that your project stays on track and delivers the results you’re after. 

Request a free FPGA project review now and let’s get started.

FAQ: FPGA and Machine Learning

When Should You Use an FPGA for Machine Learning? 

FPGAs fit machine learning applications that need microsecond-level response times and minimal power usage. These systems process data for autonomous vehicles, market trading platforms, and real-time video analysis where instant decisions matter. Edge computing and IoT systems benefit from FPGAs’ power efficiency, which extends operational time and cuts thermal management needs.

Why Use an FPGA Instead of a CPU or GPU? 

FPGAs differ from CPUs and GPUs through their hardware-level adaptability. While CPUs handle general computing and GPUs focus on parallel tasks, FPGAs let engineers build application-specific circuits. This creates precise, efficient processing paths. FPGAs use less power than GPUs, making them effective for edge AI where power limits matter. The ability to modify hardware designs also helps systems stay current with new AI advances.

How to Implement Deep Learning on FPGA? 

Deep learning on FPGAs starts with model optimization, converting to lower precision formats like 8-bit integers. Engineers use tools such as Intel’s OpenVINO or AMD’s Vitis AI to prepare models for FPGA deployment. The implementation connects with FPGA AI accelerators and custom processor designs. This method creates efficient AI processing that matches each project’s specific needs.

Are FPGAs Power Efficient Compared to GPUs? 

FPGAs outperform GPUs in power efficiency for most AI tasks. They run on 10W compared to GPUs needing 75W or more for similar operations. Performance testing shows FPGAs achieve 3-4 times better results per watt than GPUs. This efficiency suits edge systems and embedded devices where power and heat limits affect design choices.

Does FPGA Have a Future in Machine Learning? 

The growth of time-critical, power-conscious AI applications points to increasing FPGA adoption. These chips adapt to new AI methods through hardware updates, protecting long-term technology investments. Edge computing, video analysis, and self-driving systems show strong results with FPGAs. New development tools and AI-optimized architectures continue to improve FPGA capabilities for machine learning tasks.