All modern personal computers including desktops, notebooks, smartphones, and tablets, are examples of general-purpose computers. General-purpose computing incorporates ‘Von Neumann’ approach, which states that an instruction fetch and a data operation cannot occur simultaneously. Therefore, being sequential machines, their performance is also limited.
On the other hand, we have the Application Specific Integrated Circuits (ASICs) which are customized for a particular task like a digital voice recorder or a high-efficiency Bitcoin miner. An ASIC uses a spatial approach to implement only one application and provides maximum performance. However, it can’t be used for tasks other than those for which it has been originally designed. FPGAs act as a middle ground between these two architectural paradigms.
Field Programmable Gate Array (FPGA) is a semiconductor IC where a large majority of the electrical functionality inside the device can be changed during the PCB assembly process or even changed after the equipment has been shipped to customers out in the ‘field’.
FPGA enables you to program product features and functions, adapt to new standards, and reconfigure hardware for specific applications even after the product has been installed in the field — hence the term field programmable. The array of gates that make up an FPGA can be programmed to run a specific algorithm, using the combination of logic gates (usually implemented as lookup tables), arithmetic units, digital signal processors (DSPs) to do multiplication, static RAM for temporarily storing the results of that computation and switching blocks that let you control the connections between the programmable blocks. FPGA functionality can change upon every power-up of the device. So, when a design engineer wants to make a change, they can simply download a new configuration file into the device and try out the change.
The combination means that FPGAs can offer massive parallelism targeted only for a specific algorithm, and at much lower power compared to a GPU. This often makes them first choice for the development of new devices or systems. These programmable logic devices have long been used in telecom gear, industrial systems, automotive, and military and aerospace applications.
They are reprogrammable and have low NRE costs when compared to an ASIC. FPGAs reduce risk, allowing prototype systems to ship to customers for field trials, while still providing the ability to make changes quickly before ramping to volume production. However, FPGAs are less energy efficient when compared to ASICs and also not suitable for large volume productions.
FPGAs can directly be connected to inputs and can offer very high bandwidth and low latency. Low latency is what you need if you are programming the autopilot of a jet fighter or a high-frequency algorithmic trading engine: the time between an input and its response as short as possible.
This is where FPGAs are much better than CPUs (or GPUs, which have to communicate via the CPU). With an FPGA it is feasible to get a latency around or below 1 microsecond, whereas with a CPU a latency smaller than 50 microseconds is already very good. Moreover, the latency of an FPGA is much more deterministic. One of the main reasons for this low latency is that FPGAs can be much more specialized: they do not depend on the generic operating system, and communication does not have to go via generic buses (such as USB or PCIe). While FPGAs used to be selected for lower speed/complexity/volume designs in the past, today’s FPGAs easily push the 500 MHz performance barrier.
Modern FPGAs with large gate arrays, memory blocks, and fast IO are suitable for a wide range of tasks like speech recognition, artificial intelligence, next-generation wireless networks, advanced search engines and high-performance computing. Some FPGAs are essentially systems-on-a-chip (SoC), with CPUs, PCI Express and DMA connections and Ethernet controllers, turning the programmable array into a custom accelerator for the code running on the CPU.
FPGA for Military Applications
In Defense FPGA devices are used due to reduced risk, high performance, high capacity, high level of integration, highly safely and ANTI-Tamper technology. FPGA can be used to speed-up the data procession. Defense-graded FPGAs has flexibility shows the endurance of heavy workload. For modern soldier to be successful in the battlefield, it is imperative that they be equipped with gear that delivers high-tech capabilities at the lowest size and weight possible. Mission life is as key as portability, and power consumption is a decisive factor. FPGAs can provide high bandwidth radio and image signal processing, anti-tamper, and data security capabilities for smart munitions, radar, and secure radios.
Today’s secure communications devices are faced with a number of design challenges. Wireline products must meet aggressive demands for data bandwidth to achieve 40-Gbps and 100-Gbps throughput, while often providing a tamper-resistant platform for cryptographic services for applications such as the JIE. Wireless products are developed under strict requirements for reduces size, weight, and power (SWaP) to enable next-generation mobility for military radios that can simultaneously support multiple waveforms such as SRW, WNW, and MUOS. Software defined ratios (SDR) used to re-configurability all transmissions data. FPGA are natural enablers of SDR applications.
Secure communications design challenges apply to wired and wireless systems. Cryptographic functionality is an additional problem that is common to both systems. Information assurance systems with cryptographic capabilities including the Global Information Grid (GIG) support network performance in 40G to 100G and beyond. Net-centricity insures that what the warfighter sees on the ground can be linked to airborne and ground based assets. Strong encryption is key to ensuring communications and data security at ever increasing data throughput rates. Strong cryptographic algorithms implemented on FPGAs that are secure by design provide the foundation for trusted information assurance systems.
Enhanced wireless radios require interoperability and security using commercial, AES, Suite A, and Suite B encryption algorithms. These enhanced radios link firefighters, emergency, medical, and law enforcement systems together while optimizing size, weight, power and cost (SWaP-C). Next generation military-grade trusted communications and SDR platforms must generate compatible waveforms, assuring operational compatibility while at the same time supporting multiple platforms and missions with field update capability. Intel FPGAs enable wireless communications systems to meet these challenges.
Radar has been a foundational technology area in which the semiconductor industry has played a large role for the last two decades. In today’s modern radar systems, Active Electronically Scanned Array (AESA) is the most popular architecture. Going forward, next-generation radar architectures such as digital phased array and synthetic aperture radar (SAR) with ground moving target indicator (GMTI) will be the emerging technology. To achieve this, parameters such as high-performance data processing, ultra-wide bandwidth, high dynamic range, and adaptive systems needed for diverse mission requirements are some of the most common challenges to system designers. An FPGA is an ideal, and in some cases necessary, solution in addressing these challenges. The FPGA are used to transform range-azimuth onto an x-y coordinate plan in radar scan converter systems. There is a need for converting with high speed and accuracy of millions of data points
In electronic warfare, the key drivers for continuous enhancement are Electronic-counter-counter-measures (ECCM). It has the ability to rapidly analyze address multiple threads in the short time frame. The electromagnetic spectrum is used to obstruct opponents and allow allies unhindered access to the EM spectrum. FPGA offers an ideal solution to these performance requirements in critical high-speed processing, to follow different electronic attack (EA) techniques.
In electronic warfare systems, key drivers for continuous enhancements are electronic counter-counter-measures (ECCM), stealth technologies, closely interlinked smart sensor networks, and intelligent guided weapons. These systems must be able to rapidly analyze and respond to multiple threats in very short time frames. In attempting to find target signatures in broadband noise, architects are seeking to perform complex processing such as fast Fourier transforms (FFTs), Cholesky decomposition, and matrix multiplication. Multiple software-generated waveforms are then transmitted to provide false targets, while powerful wideband signals provide overall cover. These shifting tactical responses require agile, high-performance processing. The entire system frequently resides in an airborne platform and must meet strict requirements for heat dissipation along with size, weight, power, and cost (SWaP-C) constraints.
A typical system design, uses a channelizer and inverse-channelizer to process high-bandwidth input signals. The number of channels are flexible so system designers can allocate hardware resources versus system performance as needed. FPGAs offer an ideal solution to these performance requirements in the critical high-speed processing-intensive paths, a typical electronic warfare system with different electronic attack (EA) techniques.
FPGA Architecture and programming
FPGA emerged from relatively simpler technologies such as programmable read-only memory (PROM) and programmable logic devices (PLDs) like PAL, PLA, or Complex PLD (CPLD).
Modern FPGAs consist of mixes of configurable static random access memory (SRAM), high-speed input/output pins (I/Os), configurable logic blocks, and routing. The FPGA is built with mainly three major blocks such as Configurable Logic Block (CLB), I/O Blocks or Pads and Switch Matrix/ Interconnection Wires.
- Configurable Logic Blocks: These are the basic cells of FPGA. It consists of one 8-bit function generator, two 16-bit function generators, two registers (flip-flops or latches), and reprogrammable routing controls (multiplexers). The CLBs are applied to implement other designed function and macros. Each CLBs have inputs on each side which makes them flexile for the mapping and partitioning of logic. Logic blocks implement the logical functions required by the design and consist of various components such as transistor pairs, look-up tables (LUTs), flip flops, and multiplexers. Each CLB is tied to a switch matrix to access the general routing structure. The switch matrix provides programmable multiplexers, which are used to select the signals in a given routing channel and thereby connect vertical and horizontal lines.
- Programmable Interconnects — which implement routing. This hierarchy of programmable interconnection is used for allocating resources among configurable logic blocks (CLBs); where routing paths contain wire segments of varying lengths that can be connected via anti-fuse or memory-based techniques.
- Programmable I/O Blocks — which connect with external components. I/O blocks (IOBs) are used to interface the CLBs and routing architecture to the external components.
Today, hard intellectual property (IP) can be built into the FPGA fabric to provide rich functionality while reducing power and lowering cost. Some examples of the hard IP included in today’s FPGAs are memory blocks, calculating circuits, transceivers, protocol controllers, and even central processing units (CPUs). Not only are common functions that most system designers need built into the hard IP of the FPGA, but even many less commonly needed functions like high-speed serial transceivers for radar or communications, and digital signal processor (DSP) multiplier-accumulators for signal processing can be included. Today, even dual-core ARM (ARM is a brand of microprocessor designs) CPU subsystems may be built-in.
Design Flow of FPGAs
The FPGAs can be considered as a blank slate. FPGAs do nothing by themselves whereas it is up to designers to create a configuration file often called a bit file for the FPGA. The FPGA will behave like the digital circuit once it is loaded with a bit file.
First you define the requirements, and then create the architecture of the system you define. Here, you determine the components you need to implement your design. Next, you implement the system using the architecture you planned out. Finally, you verify that the system meets all the requirements.
You can further break out the steps between Define Requirements and Verify into a separate flow that can be called the software application flow. software and then integrate those applications with the hardware. After the applications are integrated with the hardware, you integrate and verify that the system meets the design requirements. Designers often must consider how their systems will run on different platforms depending on what type of application the system will be deployed into (for example, automotive, communications, and so on).
The Hardware design flow is used to program FPGAs whereas Software Design Flow is used to program the typical Microcontrollers and Microprocessors. The important steps involve in programming FPGAs are as follows.
Design entry: The description of the logic can be made using either a schematic editor, a finite state machine (FSM) editor, or a hardware description language (HDL). This is done by selecting components from a given library and providing a direct mapping of the design functions to selected computing blocks. When designs with a very large amount of function become difficult to manage graphically, HDL may be used to capture the design either in a structural or in a behavioral way. Besides VHDL and Verilog, which are the most established HDLs, several C-like languages are also available such as Handel-C, Impulse C, and SystemC.
Logic Synthesis: This process translates the above VHDL code into a device netlist format for depicting a complete circuit with logical elements. Netlist is a textual description of a circuit diagram or schematic. Synthesis involves checking the code syntax and analyzing the hierarchy of the design architecture. Next, the code is compiled along with optimization and the generated netlist is saved as a .ngc file.
Translate: This process combines all the input netlists into the logic design file which is saved as a .ngd file. Here user constraints file assigns the ports to the physical elements.
Simulation: After synthesis, the next step involves the simulation which is used to verify if the design specified in the netlist functions correctly.
Convert netlist into Binary Format: Once the design is verified, the next is to convert netlist into binary format. The components and connections are mapped to CLBs and the design is placed and routed to fit onto the target FPGA (i.e Place and Route).
Map: This involves mapping the logic defined by the .ngd file into the components of FPGA and then generating a .ncd file.
Place and Route: Here routing places the sub-blocks from the above process into the logic blocks according to the constraints and then connect those blocks.
Perform Second Simulation: To see the design quality, a second simulation is performed.
Generate Bit File: Finally a bit file is generated to load the design onto FPGA (A .bit file is a configuration file which is used to program all of the resources within FPGA).
Verify and Debug: At last, using different tools the design is verified and debugged while it is running on the FPGA.
The above-mentioned routed design must be loaded and converted into a format supported by the FPGA. Hence, the routed .ncd file is given to the BitGen program, which generates a bitstream file that contains all the programming information for an FPGA.
This is done all along with the design flow for ensuring that the logic behaves as intended. The following simulations are involved in this process:
- Behavioral Simulation (RTL Simulation)
- Functional Simulation
- Static Timing Simulation
These simulations are done in order to emulate the behavior of the components by providing test patterns to the inputs of the design and observing the outputs.
In recent years, FPGA-based accelerators have advanced as strong contenders to the conventional GPU-based accelerators in modern high-performance cloud and edge computing systems. The use of High-Level Synthesis (HLS) allows developers, using high-level languages, e.g. C, C++, SystemC, and OpenCL, to precisely configure FPGA.
Xilinx, in 2019 announced what it claims is the world’s largest FPGA, featuring 9 million system logic cells. The company’s new 16 nm Virtex UltraScale+ VU19P device incorporates 35 billion transistors to deliver “the highest logic density and I/) count on a signel device ever built,” Xilinx said, “enabling emulation and prototyping of tomorrow’s most advanced ASIC and SoC technologies,” along with test, measurement, compute, networking, aerospace, video processing, sensor fusion and defense-related applications. According to Xilinx, it has up to 1.5 terabits per-second of DDR4 memory bandwidth, up to 4.5 terabits per-second of transceiver bandwidth and more than 2,000 user I/Os. The company said the chip is 1.6X larger than its predecessor, which had been industry’s largest FPGA, the 20 nm Virtex UltraScale 440 FPGA.
In 2019, Intel announced shipments of new Intel® Stratix® 10 DX field programmable gate arrays (FPGA). The new FPGAs are designed to support Intel® Ultra Path Interconnect (Intel® UPI), PCI-Express (PCIe) Gen4 x16 and a new controller for Intel® Optane™ technology to provide flexible, high-performance acceleration. Intel Stratix 10 FPGAs are capable of 10 TFLOPS, or 10 trillion floating point operations per second. The Stratix 10 is the fastest chip of its kind in the world. At Hot Chips 2017, Microsoft officials said that using Intel’s new Stratix-10 chip, Brainwave achieved sustained performance of 39.5 teraflops without batching. Microsoft’s point: Brainwave will enable Azure users to run complex deep-learning models at these kinds of levels of performance. In addition to the PACs, Intel also offers an MCP (multi-chip package) that combines a Skylake Xeon Scalable Processor and an FPGA.
Altera and Xilinx SoCs also include ARM CPUs, but x86 processors should deliver higher performance and Intel can leverage the proprietary interconnect and 2.5D packaging technologies it has been developing. Recently, Fast Field Programmable Gate Arrays were added to Intel processors which will accelerate extreme real-time IO and machine learning. Intel® Xeon® Scalable processor with integrated Intel® Arria® 10 field programmable gate array (FPGA) is now available to select customers. This marks the first production release of an Intel® Xeon® processor with a coherently interfaced FPGA—an important result of Intel’s acquisition of Altera. The combination of these industry-leading FPGA solutions with Intel’s world-class processors enables customers to create the next generation of data center systems with flexible workload-optimized performance and power efficiency. It is not the massive breakthroughs, but these incremental improvements that lower costs, improve efficiency and drive better customer outcomes said Danny Allan, Veeam Software, member Forbes Technology Council.
While the performance of FPGA increases, the complexity also increases in architecture. FPGA Fabric, Block RAM, Embedded Registers and Multipliers, Clock management, Multi-standard programmable IO, Embedded microprocessor, Multi-gigabit transceivers, Embedded DSP-optimized multipliers and Embedded Ethernet MACs provided from Domain-optimized system logic.
The cutting-edge trend in FPGA is the insertion of special hardware by the form of hard cores. Hard cores having dedicated physical components with high frequency and fixed implementation. The resultant high-end Multiprocessors systems having high speed I/O, improved system for microprocessor development and FMC modules by adoption of FPGA. Other improvements such as scalability, re-configurability or affordability have been broaden applications, developing and custom devices for designers and entrepreneurs. Presently embedded processors are available in Hybrid FPGA/SOC are ready to be used in signal processing applications like video analysis algorithms, image processing etc. Standard processor system is combined with reconfigurable hardware for specific modules. The parallelization of hardware systems co-designed with pieces of software executed on one or more standard processors.
Data center customers increasingly use hardware accelerators, like FPGAs, when more computational speed is required from server systems running networking and cloud-based applications such as artificial intelligence training/inferencing or database-related workloads. The effective performance of hardware accelerators depends heavily on the communications bandwidth and latency between one or more server CPUs, available system memory and any attached accelerator (GPU, FPGA, application-specific standard products, etc.).
By diverting certain tasks to accelerators, more CPU cores become available to work on other higher priority workloads, increasing data center operator efficiency. Intel’s FPGA-based accelerators provide hardware-assisted performance combined with the flexibility to adapt to multiple workloads.
As noted by Intel’s Dan McNamara, “The race to solve data-centric problems requires agile and flexible solutions which can move, store and process data efficiently. Intel Agilex FPGAs deliver customized connectivity and acceleration while delivering much-needed improvements in performance and power for diverse workloads.” To meet those challenges, the new family of solutions boasts up to a 40% improvement in maximum clock speed (Fmax) when compared to the company’s Stratex 10 FPGAs.
The silicon photonics platform provides low cost, low energy and small optical interconnects resulting in integration between computational components and optical interconnects. Developers are further investigating FPGA-enabled silicon photonic interconnects for computational and processor-to-memory interconnects on work with Oracle Certified Master (OCM) architectures. FPGA-based optical network interface can execute primitives compatible with OCM data transactions.
Photonic Networks for Hardware Accelerators: Hardware Accelerators normally needs high bandwidth, low latency, and energy efficient. The high performance computing systems has critical performance is shifted from the microprocessors to the communications infrastructure. By uniquely exploiting the parallelism and capacity of wavelength division multiplexing (WDM), optical interconnects that able to address the bandwidth scalability challenges of future computing systems. The multi-casted network that uniquely exploits the parallelism of WDM to serve as an initial validation for architecture. Two FPGA boarded systems emulate the CPU and hardware accelerator nodes. Here FPGA transceivers implement and follows a phase-encoder h eader network protocol. The output of each port is individually controlled using a bitwise XNOR of port’s control signal. Optical packets are send through the network and execute switch and multicasting of two receive nodes with most reduced error.
FPGA chips to accelerate AI
FPGA computing hardware are used for data management services by Amazon’s EC2 services, Cloudera, Google Compute Engine and Hortonworks. Microsoft Azure has recently stated that they will use FPGA as one of the main processing components behind their computing hardware.
Microsoft has been using Altera FPGAs in its servers to run many of the neural networks behind services such as Bing searches, Cortana speech recognition, and natural-language translation. At the Hot Chips conference in August, Microsoft announced Project Brainwave, which will make FPGAs available as an Azure service for inferencing. Microsoft’s FPGA (Altera) system i.e. Altera translated every article on Wikipedia in less than a tenth of a second which holds 3 billion words. Here FPGA used for tuning or optimized specific task which improve its algorithm in hyper-scale applications. This flexible FPGAs accelerate servers in their massive datacenters. Baidu is also working on FPGAs in its data center and AWS already offers EC2 F1 instances with Xilinx Virtex UltraScale+ FPGAs.
Now, innovations like high-speed AI tensor logic blocks, configurable embedded SRAM, and lightning-fast transceivers and interconnects are putting this early leader back in the race. Technology advances provide a great balance of performance, economy, flexibility, and scale needed to handle today’s AI challenges, says Ravi Kuppuswamy, general manager of Custom Logic Engineering at Intel.
“FPGAs offer hardware customization with integrated AI and can be programmed to deliver performance similar to a GPU or an ASIC,” explains Kuppuswamy. “ The reprogrammable, reconfigurable nature of an FPGA lends itself well to a rapidly evolving AI landscape, allowing designers to test algorithms quickly and get to market fast and scale quickly.
Consider the el Stratix 10 NX FPGA. Introduced in June 2020, the company’s first AI-optimized FPGA family was designed to address the rapid rise in AI model complexity. New architectural changes brought the existing Stratix 10 in the same ballpark as GPUs. The new FPGA family delivers up to a 15x increase in operations-per-second over its predecessor. The boost gives exascale customers a viable FPGA option for quickly developing customized, highly differentiated end products. The new FPGA is optimized for low latency and high-bandwidth AI, including real-time processing such as video processing, security and network virtualization.
Technology innovations in today’s FPGAs enable improvements in many common AI requirements:
Overcoming I/O bottlenecks. FPGAs are often used where data must traverse many different networks at low latency. They’re incredibly useful at eliminating memory buffering and overcoming I/O bottlenecks — one of the most limiting factors in AI system performance. By accelerating data ingestion, FPGAs can speed the entire AI workflow.
Providing acceleration for high performance computing (HPC) clusters. FPGAs can help facilitate the convergence of AI and HPC by serving as programmable accelerators for inference.
Integrating AI into workloads. Using FPGAs, designers can add AI capabilities, like deep packet inspection or financial fraud detection, to existing workloads.
Enabling sensor fusion. FPGAs excel when handling data input from multiple sensors, such as cameras, LIDAR, and audio sensors. This ability can be extremely valuable when designing autonomous vehicles, robotics, and industrial equipment.
Adding extra capabilities beyond AI. FPGAs make it possible to add security, I/O, networking, or pre-/post-processing capabilities without requiring an extra chip, and other data-and compute-intensive applications.
In 2020, the global Field-Programmable Gate Array (FPGA) market size was USD 5708.9 million and it is expected to reach USD 11420 million by the end of 2027, with a CAGR of 10.4% during 2021-2027.
The FPGAs market is predicted to expand as the production volume and demand for telecommunications, data centers, and automotive products and solutions such as wireless baseband solutions, radio solutions, wireless modems, network processing cards, and electrical devices grow. FPGAs are increasingly being employed in data centers to offload and speed specialized services. This factor is predicted to propel the FPGA industry forward. ‘
The expansion of the FPGA market is likely to be aided by the significant increase in demand for data centers as a result of the increasing incorporation of IoT in many sectors. FPGA helps data centers boost their processing performance. The growing demand for efficient computing, greater scalability, dependability, and storage, as well as the use of HPC in the cloud, are projected to propel the FPGA market forward.
Furthermore, the FPGA market is expected to grow due to the increased use of FPGAs as an Infrastructure-as-a-Service (IaaS) resource by cloud customers. Several cloud service providers are using field programming gate arrays to speed up network encryption, deep learning, memory caching, webpage ranking, high-frequency trading, and video conversion.
Key factors fueling the growth of this market include the increase in the global adoption of AI and IoT, ease of programming & faster time-to-market of FPGA than ASIC, and incorporation of FPGA in ADAS.
The increasing demand for artificial intelligence and machine learning is likely to boost the FPGA market value. The introduction of nano bridge FPGA is projected to heighten the market growth, owing to technical advancements, such as being high density, which reduces the area occupied by the logic circuit.
Europe is largely focusing on industry 4.0 technology to increase the manufacturing process and productivity. This will positively influence the demand for FPGA in the region. The growing innovation in wireless communication will contribute to the FPGA market growth in 4G/5G waveform coexistence, non-contiguous carrier aggregation, and processing of centralized Cloud Radio Access Network (C-RAN).
It uses dynamic partial reconfiguration that offers higher flexibility in design time and runtime suitable for 5G architecture. Moreover, these devices are increasingly being adopted by automotive manufacturers and OEMs to build efficient safety systems, such as adaptive cruise control, collision avoidance systems, and Advanced Driver Assistance Systems (ADAS), making them more scalable. They also require the least hardware modifications for system upgrades.
Field Programmable Gate Array (FPGA) Market Segment by Applications can be divided into: Test, Measurement And Emulation; Consumer Electronics; Automotive; Wired and Wireless Communication; Industrial; Military and Aerospace; Health Care; Data Center and Computing; and Telecommunications and others. FPGA chips are majorly adopted in industries, owing to their ability to market faster and provide cheaper solutions for low to medium volume production, compared to Application-specific Integrated Circuits (ASIC), which are more expensive and time-consuming. Adaptable acceleration in data centers for storage systems and highly efficient servers are projected to drive the market growth. FPGA offers low-latency connection and customized high bandwidth for network and storage systems.
Based on type, the SRAM segment is expected to be the most lucrative. Because it allows for easy reconfiguration, SRAM is the most often used technology for programming FPGAs. SRAM-based FPGAs are created using the CMOS fabrication method, which allows for higher power efficiency and logic density than previous technologies, which is propelling the market forward.
The flash segment of the FPGA market is projected to grow at the highest CAGR during the forecast period. The key factor contributing to the growth of this segment is the nonvolatility and the low power consumption of flash-based FPGA. Moreover, these FPGAs offer resistance to radiation and eliminate the requirement of any external memory. Flash-based FPGA can also be programmed.
Based on application, the data centers & computing segment is expected to be the most lucrative. The continued usage of high-performance computing (HPC) in cloud storage, as well as significant technological breakthroughs in the fields of machine learning, artificial intelligence, and deep learning, are driving this segment’s growth.
Based on region, APAC is projected to be the most lucrative region. The increasing Internet penetration, ongoing technological advancements such as the introduction of 4G and 5G, and growing data traffic due to the rising number of technologically advanced consumer electronic devices and connected device users are all contributing to the market’s growth in this region. The region has a substantial presence of major semiconductor foundries that provide manifesting services to FPGA firms. The APAC FPGA market is likely to be driven by the telecommunications, industrial, automotive, consumer electronics, and computer industries.
This growth can be attributed to the increased adoption of IoT and machine-to-machine (M2M) communication in the industrial and automotive sectors. FPGAs offer parallel processing and reprogrammability features, which make them suitable for these applications.
APAC houses some of the major semiconductor foundries such as Taiwan Semiconductor Manufacturing Company (TSMC) (Taiwan), United Microelectronics Corporation (UMC) (Taiwan), and Samsung Foundries (South Korea), which drive the growth of the FPGA market in the region. Moreover, the increasing number of smartphone users in countries such as China and India is expected to drive the growth of the FPGA industry in APAC during the forecast period.
The FPGA market in China is growing at a fast pace, owing to the presence of established automotive and consumer electronics players in the region. Furthermore, companies, such as Alibaba, Samsung, Xinhua, etc., in this region are heavily investing in AI, contributing to the demand growth for these chips. For instance, in February 2019, Xinhua introduced the world’s first AI news anchor.
Key players in the FPGA market include companies operating at different stages of the value chain. These companies include Xilinx, Inc. (US); Intel Corporation (US), Microchip Technology Inc. (US); Lattice Semiconductor Corporation (US); QuickLogic Corporation (US); Efinix, Inc. (US); Flex Logic Technologies, Inc. (US); GOWIN Semiconductor Corp. (China); Achronix Semiconductor Corporation (US); S2C, Inc. (US); Leaflabs, LLC (US); Adlec, Inc. (US); BitSim AB (Sweden); ByteSnap Design (UK); Enclustra GmbH (Switzerland); EnSilica (US); Gidel (US); Nuvation Engineering (US); Selexica, Inc. (Germany); and EmuPro Consulting Private Limited (India). These companies focus on adopting both organic and inorganic growth strategies, such as product launches and developments, partnerships, contracts, collaborations, and acquisitions, for strengthening their position in the market.