Using FPGAs for demanding applications

Developers of the Seemingly Impossible
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Wherever you see incredibly fast, leading-edge technology like autonomous driving, electronic trading, and satellite communications, look for FPGAs. The processing capabilities, massive bandwidth, and energy efficiency of FPGAs is allowing designers to create increasingly high-performance solutions for many market segments and industries. 

In this article we’ll explore what you need to know about deciding when and where to use FPGAs:

How the FPGA landscape is changing rapidly

Pros and Cons of FPGAs 

How different industries are using FPGAs in high-performance applications 

When to Use FPGAs or CPUs

 

How the FPGA landscape is changing rapidly

Look at what’s happened in just a few years:

  • In December 2015, Intel bought Altera for $16.7B making it its largest acquisition ever
  • Microsoft is now using FPGA in its data centers 
  • Amazon is offering FPGAs on their cloud services 

The global FPGA market in 2019 was valued at $9B and is projected to grow near 10% CAGR over the next 7 years.  

The January 2020 article Could FPGAs Outweigh CPUs in Compute Share? from nextplatform.com says, "According to Xilinx CTO, Ivo Bolsens … FPGAs won’t just gain incremental momentum, they will put the CPU out of work almost entirely. ‘In the future you will see more FPGA nodes than CPU nodes. The ratio might be something like one CPU to 16 FPGAs,’ Bolsens predicts, adding that it’s not just a matter of device numbers, ‘acceleration will outweigh general compute in the CPU."

 

Pros and Cons of FPGAs

When an application requires a lot of information to be processed quickly, FPGA solutions offer higher performance compared to standard CPUs. Although the use of standard CPU platforms is still the most flexible option, in some demanding applications the use of FPGA solutions is fully justified.  

Pro FPGA

The main reasons for using FPGAs instead of CPU-based solutions are:

  • Low Latency
    FPGAs can compute and process data extremely rapidly.
  • Throughput
    FPGAs have massive I/O bandwidth and can continuously process huge amount of data thanks to their parallelism capabilities.
  • Flexibility
    FPGAs are used to execute algorithms at hardware-level processing speeds with the flexibility to change the algorithms in milliseconds.
  • Energy efficiency
    In some applications, FPGAs will provide some significant energy savings over traditional CPU-based solutions.

How do FPGAs deliver such high performance? As explained in The Principles of FPGAs on electronic design.com, "Unlike processors, FPGAs are truly parallel in nature. Each independent processing task is assigned to a dedicated section of the chip. Therefore, the performance of one part of the application is not affected when more processing tasks are added."

Spider chart comparing CPU and FPGA

FPGA Cons

Despite all the advantages, there are factors that limit the use of FPGAs on a larger scale:

  • Cost
    FPGA solutions are still generally significantly more expensive than solutions based on standard CPUs.  
  • Limited developer resources
    It can be difficult to program FPGAs using RTL languages (Verilog, VHDL) given the limited number of developers possessing this expertise.  

The High-End FPGA Showdown series (part 3 Design Tools – Where it All Begins) on eejournal.com states, "Engineers with software-only backgrounds often fail to appreciate the complexity involved in using these devices and the long learning curve required to gain enough proficiency to use FPGAs near their capability…Taking full advantage of FPGAs requires digital logic to be designed, and, despite decades of progress, we are not yet at the point where FPGAs can be optimally used without at least some degree of hardware expertise in the design process."

To overcome these challenges, the two main FPGA manufacturers, Xilinx and Intel, are trying to democratize access to FPGAs by offering design tools that allow FPGAs to be programmed using more standard programming languages, such as C, C++ and Python, accessible to a larger pool of developers. 

And, there is a growing ecosystem of solutions available that allow developers to accelerate development times without having to reinvent the wheel.  Some vendors now offer complete framework solutions that integrate multiple FPGA modules (cores) along with software drivers and libraries enabling system developers to focus their development on their applications and differentiators.

 

How different industries are using FPGAs in high-performance applications

FPGAs have been deployed at massive scale in data centers, military and aerospace applications, telecommunication infrastructure, healthcare, and now in ADAS and autonomous vehicles. 

Which applications are best suited to use FPGAs?  We’ll provide a few examples for each of these key verticals:    

 

Aerospace & Defense

The aerospace and defense market is generally less sensitive to cost compared to consumer applications, making FPGAs even more likely to be adopted by manufacturers in this segment.

Examples of Aerospace & Defense FPGA applications:

  • Phased array RADAR and satellite communication systems must be capable of processing and computing an enormous quantity of data in real time, which makes them ideal examples of good uses of FPGAs. Phased array technology uses an arrangement of antenna elements where the relative phase of each element is varied to steer the radiation pattern.  This architecture requires massive I/Os bandwidth as well as high-performance Digital Signal Processing (DSP) capabilities, which can only be realized using a hardware solution such as FPGA.  
  • Missile guidance systems and other military applications use FPGA for low latency.  
  • Electronic warfare systems and secure communication systems such as network encryptors and wireless radios use FPGA technologies to take advantage of high throughput processing capabilities and re-configurability.

Find out how programmable 4G technology was leveraged into FPGA and Network Processors to develop high-capacity multichannel, multiband, point-to-point (PTP), point-to-multipoint (PMP) and mesh radio systems used in public safety and defense applications in this case study.

 

Telecommunication Infrastructure

As we enter the 5G communication era, the next generation of wireless technology will present huge opportunities for businesses, industry, and consumers. 

We’re seeing 5G communication offering multiple capabilities, including

  • ultra-fast data rates
  • ultra-low latency
  • massive device connectivity 

To overcome complex technical issues and continuously evolving standards, Xilinx’s latest FPGAs (Versal™) have been used by Samsung to build a universal, flexible, and scalable platform that can address multiple operator requirements.  In this example, FPGAs are used to provide

  • required bandwidth 
  • low latency performance 
  • optional re-programmability as the 5G radio standards evolves and new features gets released  

In the long run, manufacturers of 5G commercial base stations are likely to eventually replace FPGA devices by converting them into ASIC devices.  Such conversions will generally provide power consumption improvement and huge cost reduction savings for commercial base station manufacturers.

 

Data Centers (Hardware Acceleration)

Using FPGA for hardware acceleration in data centers has gained a lot of traction recently.  Hyperscale customers are increasingly adopting FPGA to cope with increasingly complex workloads across compute, storage and networking applications.  

Here are a few emerging applications that take advantage of FPGA in data centers to accelerate their workload:

  • Financial technology
    Tick-to-trade electronic trading, market data processing, pre-trade risk checks, smart order router
  • Network acceleration
    Encryption, compression, deep packet inspection, virtual switching and routing
  • Video and imaging
    Video transcoding, live streaming, image processing
  • High Performance Computing
    Genome sequencing, scientific simulations, machine learning

Achieving maximum throughput and ultra-low latency performance while having the flexibility to reconfigure the hardware is paramount for financial technology and network acceleration applications.  Video, imaging, and HPC applications leverage FPGA technologies for their high throughput, power consumption efficiency and re-configurability to adapt to evolving workloads.

Learn how FPGAs were used to develop ultra-low latency and ultra-low gate count Ethernet MAC/PCS and RS-FEC IP Cores for latency-critical applications including algorithmic trading in this case study.

 

Automotive - Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD)

Automated driving systems require the highest level of safety to fully enable the new world of mobility. Automotive-grade FPGAs and MPSoCs (FPGAs with Embedded ARM processors) deliver high performance and low latency to enable safety-critical functionality in the new era of Autonomous Driving.

Discover the 4 challenges engineers face when designing with Xilinx Zynq MPSoC and Intel SoC FPGAs. 

The various sensors in vehicles generate massive amounts of data, all of which must be merged into a single data pipe for real-time processing. The data aggregation, pre-processing and distribution capability of FPGAs and MPSoCs helps improve AI processing by merging sensor data and efficiently preparing it for vehicle control.  

Thanks to their I/O bandwidth, massive computing capabilities, low-latency performance, and reconfigurability, automotive-grade FPGAs and MPSoCs are ideal for implementing ADAS and AD functionalities.

Key examples of Automotive FPGA and MPSoC solutions:

  • Automated Valet Parking (AVP)
    In December 2019, Xilinx announced that their MPSoC will be used in Baidu’s Apollo Computing Unit (ACU) for sensor fusion and deep learning inference processing required to handle the complex driving environment of AVP.  
  • Advanced Driver Assistance Systems (ADAS)
    Subaru has selected Xilinx’s automotive qualified MPSoC for their next-generation vision-based advanced driver assistance system.  
  • LiDAR solutions with 3D data imaging
    Multiple startup companies are also using FPGAs and MPSoC to develop automotive LiDAR solutions that can deliver accurate, real-time 3D data imaging.

 

Healthcare

Artificial intelligence and machine learning technologies are contributing to smarter, more efficient and effective solutions for the healthcare industry. The quantity of data, calculations, and images that need to be processed quickly make high-performance FPGA applications ideal for medical solutions. 

Examples of Healthcare FPGA solutions:

  • Imaging
    High resolution image processing in real time is used in scanners, diagnostic machines, and other essential medical equipment.
  • Vision Systems
    Reliable vision systems are critical for surgical solutions including robotic assistance.
  • Monitors
    Fast response time and reliability make monitoring devices such as automated external defibrillators and clinical defibrillators more efficient and accurate.
  • High Performance Computing
    As noted in the Hardware Acceleration section of this article, genome sequencing, scientific simulations, and machine learning all benefit from FPGA performance.

Find out more about the breakthrough innovation behind a digital mammography detector that uses an FPGA design for a highly programmable image processing engine in this case study.

Additional benefits that FPGAs offer to medical solutions are security and the ability to update functionality. The wired.com article Most Medical Imaging Devices Run Outdated Operating Systems says, "health care providers can make updatability a major priority in procurement to push manufacturers toward more flexible designs."

 

When to Use FPGAs or CPUs

To take advantage of the performance of the FPGA and the flexibility of the CPU, some products combine both.  An example would be to use an FPGA to continuously process, at full line rate, millions of packets per second by decoding each packet and extracting meaningful information that can be effectively presented to multiple Virtual Machines (VM) running on CPUs.  Software solutions can then be deployed on CPUs to enable detailed analysis of the data collected to extract higher level information that can be presented to the end users.

Discover how a cost-effective FPGA/CPU solution was developed for unique cloud-based digital advertising applications in this case study.

 


  1. https://newsroom.intel.com/press-kits/intel-acquisition-of-altera/#gs.f56ae6
  2. https://www.microsoft.com/en-us/research/project/project-catapult/
  3. https://aws.amazon.com/ec2/instance-types/f1/
  4. https://www.grandviewresearch.com/industry-analysis/fpga-market
  5. https://www.xilinx.com/news/press/2019/xilinx-and-samsung-jointly-enable-the-world-s-first-5g-nr-commercial-deployment.html
  6. https://www.xilinx.com/news/press/2019/xilinx-powers-baidu-s-production-ready-acu-advanced-platform-for-automated-valet-parking.html
  7. https://www.xilinx.com/news/press/2020/subaru-selects-xilinx-to-power-new-generation-eyesight-system.html
  8. https://www.xilinx.com/applications/automotive/adas.html#lidar
Alexandre Raymond serves as Chief Technology Officer (CTO) at Orthogone Technologies. In this role, he is responsible for driving the vision of Orthogone’s research and development activities as well as the development of technologies and intellectual property in collaboration with Orthogone’s clients. He brings with him more than 20 years of high technology management and design experience.
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