How is a Next Generation of AI Technology Going to Use FPGA Chips to Enhance Machine Learning
Field-programmable gate array (FPGA) technology is an integrated circuit that can essentially be programmed on the go after it is manufactured. FPGA chips have gained a lot pf prominence with the rise of artificial intelligence (AI) and machine learning. One big question that has to be answered is whether FPGA hardware can enhance machine learning and contribute to the perfected artificial intelligence of tomorrow.
Artificial Intelligence is Shaking Up the Chip Market
FPGA chips are shaping up as the biggest rival to graphics processing units (GPUs). The demand for GPUs had been growing quickly over the past few years. Data centers that focus on AI are one of the biggest consumers of GPUs today.
Because of this demand, companies like Nvidia have seen a massive revival. According to an interview that Nvidia founder Jen-Hsun Huang gave for Economist. AI changed up the market, leading to a GPU revival.
But while GPUs are gaining some prominence, they’re not the only player on the market. The need for FPGA chips is also increasing. The fact that they’re field-programmable is the one that has contributed to their popularity. FPGAs offer a lot of flexibility, which is why they’re a solution of preference as far as AI and machine learning are involved.
While FPGAs are considered trickier to handle, they have already been added to multiple servers. Microsoft is one of the giants making the switch.
This is probably one of the reasons why massive market shifts have occurred over the past few years. A few prominent mergers and acquisitions have also occurred. In 2015, Intel acquired Altera, a prominent FPGA manufacturer. The value of the deal was the rather impressive 16.7 billion dollars. In August of the same year, Intel bought Nervana. The startup focuses on the development of specialized artificial intelligence solutions and the value of the deal was over 400 million dollars.
According to Intel representatives, new computing workloads necessitate innovative solutions. This is one of the main reasons why specialized hardware like FPGA chips are going to become even more crucial for AI development in the future.
The Benefits of FPGA Technology for AI Development and Machine Learning
One of the biggest benefits of the FPGA technology has been mentioned already, its flexibility.
Evidence so far suggests that www.directics.com/artix7/ can accelerate artificial intelligence workloads. The integrated chips can be both programmed and reprogrammed for the execution of specialized tasks. FPGAs do not require the use of an operational system, which contributes to additional burden. The hardware circuits created this way are committed to an individual task for the execution of the respective program. When it comes to processing large volumes of data, such a mechanism makes a lot of sense.
Over the years, FPGA hardware has been seeing consistent improvements. This is yet another reason why the technology is being vastly implemented in the field of AI development.
Through these improvements, they have made it possible for a system to gain speed and decrease processing time without the need for software functionality. In essence, such chips allow for hardware rather than software implementation.
Doesn’t a hardware implementation lack flexibility? The answer is no, reprogramming can occur whenever deemed appropriate. The FPGA can be customized to meet all requirements of the system that such chips are embedded in. As a result, the performance is much better than in the case of systems that require software based on an OS.
Whenever hardware implementation occurs, repetitive tasks are particularly easier and faster to perform. Needless to say, such repetitive tasks often form the core of machine learning.
Will FPGA Chips Become the Hardware of the Future?
In 2017, an interesting presentation took place as a part of the International Symposium on Field-Programmable Gate Arrays. According to the authors of the presentation, FPGA chips are the future of deep learning implementations, especially when it comes to artificial intelligence development.
As a part of the presentation, researchers evaluated deep neural network (DNN) algorithms on two generations of Intel FPGA chips in comparison to Nvidia GPUs.
The GPUs demonstrated remarkable performance when deep learning frameworks have to be processed. This is yet another reason why Nvidia has seen its revival over the past few years. The performance, however, comes at the cost of rigid requirements. High volumes of data and energy costs are just two of these non-negotiable requirements for an optimal performance.
FPGAs also performed quite well in the world of DNN research. The researchers concluded that such technology is rather applicable in the world of artificial intelligence. Big data and machine learning also feature among the areas in which FPGA use has potential. The FPGA chip tested by the team of researchers outperformed GPUs in the fields of compact data types. The adaptability and flexibility also contributed to an excellent performance as far as other kind of data were concerned.
An existing chip is very easy to reuse via reprogramming, another massive advantage noted by the research team. Thus, the amount of time required to go from idea to prototype development is reduced from 18 to six months in the case of FPGA use.
Intel Senior AI Product Specialist Bill Jenkins said in an interview that software development is taking place for fixed architecture. The code is optimized for it. In the case of FPGA use, a specific architecture is developed for the respective problem. Data does not have to go through the CPU anymore, it can go through the FPGA directly. The processing time is reduced, a massive advantage when it comes to machine learning and handling massive volumes of data.
FPGAs offer both unified tools and workflows, essentials in the world of developing the next generation of artificial intelligence. Powerful, efficient computation solutions can be developed this way to address the complex analytical tasks of today. Only the hardware is required, which is obviously the key to maximizing system performance.
FPGAs have been around for nearly three decades. Artificial intelligence has contributed to their rebirth today. Equipped with a huge amount of internal memory bandwidth, FPGAs can get information in very quickly and produce results with minimal latency. New and new applications are emerging with FPGAs being brought to the software world and FPGA as a service emerging as a brand-new concept. Only time will tell what the scope of application is going to be and how tech giants will perfect FPGA technology in the years to come.