Description

Field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and application-specific standard products (ASSPs) are logic devices that are typically used to manage the interchange of digital signals within a system. While ASICs and ASSPs result in a lower unit cost, they are associated with high fixed costs and demand high volume manufacturing.
Compared to ASICs and ASSPs, FPGAs are more standardized logic components that can be offered to multiple users for a wide range of applications, making the technology more cost-efficient for low and medium-volume production. Also, FPGAs provide higher design flexibility and require lower time-to-market adding to the competitive technology benefits.
The proliferation of Internet-of-Things (IoT) has triggered the demand for programmable logic devices such as FPGAs for controlling, connecting, and computing applications across multiple industries such as communication, aerospace & defense, automotive, consumer electronics, industrial, and medical.
FPGA is an oligopoly structured market dominated by a handful of large companies. Expanding their existing FPGA product line is no longer the only growth opportunity focus area for these companies. The companies are focused on developing multiple product lines and digging into newer applications that have been created by emerging industry trends.
Large-sized companies are aggressively investing a significant portion of their revenue into R&D efforts to develop innovative FPGA-based product lines. Prominent areas of innovation include smaller form factors, low power consumption, low-cost products, and shorter time-to-market. In addition, the companies are also actively looking at acquiring new companies and venturing into disruptive start-ups in order to expand their existing product portfolio venture into newer markets.
While there is a high demand for innovative hardware solutions, large companies are leaning toward a platform-based approach based on FPGA fabric, leveraging heterogeneous processing capabilities to handle various artificial intelligence (AI)/machine learning (ML) workloads. Ultimately it is not only about creating new products to address existing industry challenges but also about foreseeing challenges and build sophisticated platforms to address the challenges that would be posed by future IoT applications.