FIELD PROGRAMMABLE GATE ARRAY MARKET ANALYSIS

Field Programmable Gate Array MarketSize and Trends

The field programmable gate array market size was valued at USD 9.7 Bn in 2023 and is anticipated to witness a compound annual growth rate (CAGR) of 7% from 2023 to 2030. The market is to propel the market as there is a Rising demand for internet of things. Based on the segment the market is divided by configuration, by architecture and by end user type.

Field Programmable Gate Array Market Trends

• The emergence of new technologies: The emergence of new technologies like artificial intelligence and 5G networks is expected to have a significant influence on the field programmable gate array (FPGA) market in the coming years. As AI workloads are becoming increasingly compute-intensive, there is a growing need for specialized hardware that can accelerate AI processing more efficiently than generalized processors. FPGAs are well-suited for this role as their highly parallel architecture allows them to speed up workloads like machine learning inference and computer vision processing more than CPUs or GPUs alone. Many major FPGA vendors have recently rolled out new products with enhancements targeted specifically at AI such as high-bandwidth memory interfaces and integrated machine learning engines.
• Growing Adoption of FPGA For Applications Requiring High Performance And Parallel Processing: The growing adoption of Field Programmable Gate Arrays or FPGAs for applications requiring high performance and parallel processing is having a profound impact on the FPGA market. FPGAs have seen increasing use in domains like high-performance computing, data analytics, artificial intelligence and machine learning in recent years. This is because FPGAs allow customizable, parallel processing which provides significant speed and efficiency gains over general purpose processors for these kinds of workloads. For example, several top AI and ML training organizations have extensively implemented FPGAs in their infrastructure to achieve faster, more power-efficient model training. In 2021, Anthropic, an AI safety startup, reworked their neural network training platform to leverage FPGAs from Intel and achieved a massive 400x increase in operations per second compared to GPU only servers. This helped them train models much more rapidly while reducing carbon footprint. Other companies in genomics, autonomous vehicles, fintech have also reported speedups of 10-100x by offloading portions of their workloads to FPGA accelerated servers.