Field programmable gate arrays (FPGAs) have always been highly flexible, but with the increasing processing power in ever smaller integrated chips, FPGAs are more adaptable than ever. Because they are more malleable in their ability to change computing architectures, FPGAs can help create tailored software solutions for customers. The FPGA market is growing at a brisk rate in applications including smartphones, artificial intelligence (AI), data analysis, multidimensional warfare electronics systems, telecommunications, the automotive industry, wireless networks, and more.
According to the FPGA market is forecasted to be worth $9.5 billion by 2023. Low-end FPGAs hold the promise due to their benefits in size, low-power consumption, performance, and numerous advanced features. FPGAs can also accelerate time-to-market, edging out ASICs, which can require 18 months to production.
FPGAs Enable Dynamic Data Centers
FPGAs implemented within a data center can both rapidly scale and dynamically configure resources, so that they are optimized for applications such as video processing, enterprise data analytics, and AI. Data centers can now dynamically construct needed resources using bare-metal FPGAs to construct high-performance servers without interrupting other running processes.
FPGAs vs. GPUs
FPGAs compete with GPUs in the acceleration of deep learning algorithms. An enormous number of GPUs can process data in parallel. However, FPGAs also process data algorithms in parallel on a massive scale and can exceed the efficiency of GPUs. According to Gartner research, 30 percent of all B-to-B businesses world-wide will use at least some AI in a sales process by 2020. Increasingly crucial to sales processes, voice recognition, and image recognition are beginning to leverage AI and FPGAs to accelerate AI processing. AI accelerators can be GPUs, ASICs (custom chips), powerful CPUs, or FPGAs. Accelerators assist AI by off-loading the main processor via the rapid execution of numerous computations. FPGAs also create an advantage in AI. Due to rapid developments in new algorithm, it is necessary to update the AI software to optimize system performance and FPGAs are uniquely suited to the task.
FPGAs have low latencies since a program runs on “bare metal” versus having to shuttle data through a software stack, including handling by operating systems. Each AI accelerator platform has benefits. However, FPGAs are enormously flexible and hold a low latency advantage over GPUs and CPUs. New development tools for programming FPGAs in AI applications are becoming available as AI gains traction.
FPGA Applications
Other applications are utilizing FPGAs, trending towardj the acceleration of many virtualized workloads. One example is the rapidly growing demands in how we use networks for transferring data. FPGAs will play a role in the formidably complex 5G networks. The ability to lower latency with an FPGA is attractive, as data sharing in connected automobiles, smartphones, smart cities, and more, is increasingly important. In these applications, FPGAs perform crucial work as accelerators for offloading CPUs, reducing utilization by as much as 50 percent.
Xilinx, an FPGA-focused company, has revealed a new platform that contains FPGA logic fabric called Adaptive Compute Acceleration platform which offers software programming scalable and adaptable to changing workloads and multiple-use cases dynamically. FPGAs have become key to creating new ways of dealing with the rapid growth of different types of data and data processing across a variety of applications.
Networks of FPGAs can offer continuously adaptive solutions from composable hardware on an ultra-low-latency fabric. Optimizing the computing platform for innovative applications that use AI, image processing, data analytics or other new applications using FPGAs provides an efficient, thoroughly adaptable, high-performance and low-latency architecture that can’t be beaten with GPUs or ASICs, because neither offers dynamically configurable hardware like FPGAs.