The FPGA market continues to boom. According to the global forecast, over the next few years, its CAGR will be at an average of 8.6%. But the most interesting are new appliances of the tech, which are sometimes more akin to science fiction than to real life.
Table of Contents
- What FPGA Is?
- FPGA Main Features
- Future of FPGA: Predictions by Promwad
- Conclusion
What FPGA Is?
FPGA means Field-Programmable Gate Array; a type of programmable circuit. Usually, ICs come unchangeable once they are manufactured, but FPGAs don’t. Every time an engineer devises changes in the code, the circuit changes its configuration, respectively. So, the companies produce intermediate products, and engineers then adjust them to their specific needs.
Such an approach puts FPGA between the hard-wired and unchangeable ASICs and much more agile CPUs (or GPUs, which are very close). Due to ASICs are highly specialized, they work faster and cost cheaper when mass-produced. But when the volume is lower, the cost becomes way higher.
CPUs are highly universal and can make everything the program contains within the scope of its instructions. However, such flexibility makes them slower and much less power-efficient.
FPGA is the middle ground. The technology weaves together the efficiency level close to ASIC with flexibility comparable to CPU and even higher. This is the main reason why the popularity of FPGA grows rapidly.
FPGA Main Features
Pros:
- Extremely high flexibility. Thanks to the chip being fully reconfigurable, FPGA is not limited by the set of instructions like CPU. With that said, it has its own restrictions based on a limited variety of available primitives. Coupled with a high number of I/O ports available, it helps to use the chips where the standards are changing very fast.
- High processing speed. Once programmed, FPGA acts as a highly specialized self-sufficing device with pretty low latencies. Meanwhile, the CPU requires an external memory to execute the program, making the device slower.
- Low power consumption. FPGAs are in fact 3-4 times more efficient than the CPUs (or GPUs).
- Small size. FPGA chips can be rather small for a performance they have in thanks to a high specialization.
Cons:
- Expensive coding. There are many more software developers in the world than those who are familiar with HDL languages. That's why HDL coders are more costly hires. By the way, it is a reason for the fast adoption of high-level synthesis - a process that helps to configure FPGAs using high-level programming languages.
- Lack of backward compatibility. In some cases, old programs won't work on newer devices without reworking.
- Expensive, after all. Despite low consumption, FPGAs can be pretty expensive in production. Sometimes it’s enough to overlap the possible saving during all the lifetime.
Future of FPGA: Predictions by Promwad
We at Promwad have been familiar with FPGA design from the very beginning, and finished dozens of projects successfully. Analyzing this experience, we found some interesting patterns. Combining our findings with the actual tech trends, it becomes possible to predict the nearest future of FPGA. And the next couple of years seem rather interesting and promising for this technology.
Let's take a look.
5G and Telecom
Nokia, Ericsson, and other telecommunications equipment manufacturers are currently hiring FPGA engineers from different countries. Want to ask why? Because the mass adoption of 5G has already started, but the standard itself remains unfinished. It means that any changes will be applied on-site, I mean directly in сell towers and distribution frames.
Picture credit: freepik.com
Sky-high speeds and vast amounts of data require an enormous volume of parallel computing. Meantime, the rapidly changing environment makes it impossible to use more efficient ASICs. And flexible and fast FPGAs look like the best fitting solutions for such purposes.
However, the speed of HDL-development and testing remains low, which can negatively affect the plans of telecom companies. As well as grand plans of achieving the incredible 100Gbps milestone.
Internet of Things
FPGA circuits have many features that are fitting to the goals of IoT. They consume a bit of power on computations. They’re fast and able to work highly concurrently. They’re compact yet have plenty of I/O ports. However, the fact is that most FPGAs are too expensive to be implemented into IoT devices.
A couple of years ago FPGA manufacturers began to eliminate that bottleneck. Here are several of the most simple and cheap models of FPGA. The cheapest to date are the Lattice iCE40 Ultra/UltraLite family and GOWIN GW1NZ-ZV always-on ultra-low-power FPGA. With a cost reaching as low as a few dollars per piece, such circuits are acceptable for the IoT devices a bit more sophisticated than connected light bulbs. In the next few years, the price will continue to decrease, while the volume of data will multiply. It means that the role of FPGA in the world of IoT will certainly increase.
Neural Networks and Machine Learning
Over the last several years, neural networks became ever more sophisticated. But now the trend is in miniaturization. The epitome of it is Huawei’s new mobile processor HiSilicon Kirin 980 with an integrated neural network processing unit. Yes, you can run AI within your smartphone, not only within the huge Amazon server. This sounds quite promising.
Using FPGA to build a neural network infrastructure is a good idea. As long as weights are stored inside the SoC, the network consumes much less power and works much faster. An example of a well-applicable FPGA for neural network processing is Zynq UltraScale+ MPSoC. Using such circuits is a way to place millions of ALU (arithmetic and logic units), equipped with internal memory, within one mobile (or at least portable) device to make the neural networks fully autonomous.
Xilinx works in this direction among others. Intel has its own family of the special AI-oriented FPGAs, coupled with the development toolkit. Into the bargain, the company has developed a line of ready-made acceleration platforms based on their own circuits. The Xilinx Alveo accelerators family is pretty similar in the terms of tasks they cover.
Robotics
Robotics is a domain where FPGA is suitable the most, and the situation will even change to the better through time. A lot of I/O ports, fast processing, low latency, and high flexibility make FPGA chips one of the best here.
An essential trend of robotics is increasing complexity. It means, above all, a growing volume of connected sensors, which require more and more input-output ports. All the data from these sensors must be processed in real-time, which requires both high processing speed and high concurrency.
Of course, FPGA devices with high performance have the same high prices. But robots are very expensive, too, which means price issues are almost nonexistent here. Also, in the case of successful R&D robots can come to the mass market, and FPGA can be switched to a much cheaper ASIC.
Data Centers
CPUs keep the main role in the data centers, but the situation is changing. The main issue is that the traditional architecture of data centers have become less effective. A constant increase of power consumption, paired with a declining effectiveness factor, leads engineers to seek a solution. The solution is to build hybrid data centers with different types of computations for different kinds of tasks.
FPGA technology can take the central role here. Having its own built-in memory, high parallelism, as well as extremely high processing per watt rate, FPGA circuits can handle the main part of computing tasks in the new data center paradigm. Well-known Xilinx is one of the pioneers of the changing process here. The company was the main sponsor of The Next FPGA Platform conference held in San Jose at the beginning of 2020.
But the changes in this field are already in full swing. It is worth mentioning that all the internet giants like Amazon, Microsoft, Google, Alibaba, and others, are already familiar with Intel and Xilinx FPGA accelerators which improve the efficiency of data centers.
Connected and Driverless Cars
Autopilot is one of the hottest trends these last couple of years. But while it is just on its way to adoption, the connected cars are already everywhere. Moreover, according to Goldman Sachs forecast, all the cars manufactured in the developed countries will become “smart” by 2025.
Waymo driverless car. Picture credit: wikimedia.org
Technically, a significant part of connected devices in cars are just specialized IoT. What’s to the autopilot, it is a combination of different types of sensors, radars and cameras, V2X communication paradigm, and neural network-based AI. A need to process huge amounts of data in real-time is the main driver of FPGA adoption in the automotive domain. The fact is, technology will be in high demand here at least in the next few years.
Healthcare and Diagnosis
Today’s MedTech industry is closely associated with data analysis. The volume of data is constantly becoming higher and higher. On the one hand, it opens up brand new possibilities. For example, mapping of the human genome makes it possible to predict various types of cancers even before its diagnosed. On the other hand, it means great challenges for engineers who make the MedTech tools to work.
The analysis of data using stationary devices isn’t a hard task today. The most challenging question here is how to analyze the data from different sensors in real-time using wearable tools. Such tools make it available to control the parameters of our body and detect diseases at an early stage. And the role of FPGA circuits here will increase for at least the next 5-10 years.
Finances, Trading, and Insurance
From a technical point of view, the insurance sector is similar to the financial sector. Both are closely tied to data analysis and predictions. For example, the credit decision process in banks is based on the review of many different parameters to score the potential client. The probability of loss in the insurance field appears the same.
As we said before, FPGA chips are good at such tasks: the more data you need to analyze, the better FPGA fits. If we discuss trading, the speed of analysis is crucial. As you know, technology is right here too. We think that in the next few years, a new type of FPGA-based data center for banking, FinTech, and insurance industries will arrive.
HFT (High-frequency trading) is staying apart from the traditional financial operations, but covering about half of all stock trading operations. HFT is featured with millions of high-volume deals that are lasting several seconds each, if not less, but the profit from a single deal is pitiful compared with its volume. The crucial resource here is speed. That’s why HFT data centers are often situated as close to the market data sources as possible: it helps to minimize latencies and catch an advantage for a couple of milliseconds.
Picture credit: pxfuel.com
The value of speed is so tremendous, so using the CPUs executing software algorithms is the shortest way to lose. The HFT evolution is highly similar to the arms race: even a bit of advantage can seal the fate of competition. The FPGA technology helps to exclude all unnecessary operations on the hardware level, and run all possible processes simultaneously; saving valuable nanoseconds. In the next few years, the race will continue. The only thing that can stop it is legal regulation.
Industrial Manufacturing and Construction
Both industries are pretty similar in the case of FPGA usage. One of the main differences is in the level of digitalization. Construction is among the least developed industries, along with agriculture. Industrial manufacturing is much better equipped with new technologies. But the methods for how to improve the performance are mostly the same in these two domains.
Despite the fact that manufacturing contains fewer project activities, there are many different estimations and predictions in both domains. For example, it can be an error prediction based on historical events or accidents prevention. Based on OSHA data, 67% of deaths at construction sites can be easily prevented using AI. And using FPGA-based devices will be extremely efficient here.
The main reason for FPGA usage is the data for analysis is coming mostly from multiple sensors, cameras, and other registering devices. And its real-time processing is a task for fast circuits with a high level of parallelism.
Conclusion
The increasing level of technological development opens excellent possibilities for different kinds of businesses. Now we are at the brink of a new threshold of the next round of technological advancements.
FPGA will play a crucial role here. And Promwad, as a skilled FPGA development contractor, can help your business achieve new horizons in the competitive battlefield.
Be among the early adopters who are ahead of the curve!