An FPGA (Field Programmable Gate Array) is a specialised hardware array that has a wide range of applications from image processing to machine learning. An FPGA is not a chip, but, in fact, an integrated circuit that you can program to perform tasks. It’s like a huge blank canvas or sheet of paper. You can draw or create whatever you want on this sheet of paper and it will work exactly the way you drew it.

An FPGA has multiple logic gates that are connected through wires. You can connect these gates in any way you want and get the desired result. The thing that makes an FPGA special is that it doesn’t just have to do one task; you can reprogram it to do something else entirely, depending on what you need.

The best part about an FPGA is that it can be programmed to perform any kind of mathematical operation. This makes it very flexible in terms of what it can do and how fast it performs certain tasks. For example, when we try to train our machine learning models, we often use GPUs for faster computations.

FPGA stands for Field Programmable Gate Array. It is a kind of chip that can be programmed to perform various functions after manufacture. It is not a processor but an interconnection of logic gates that can be configured using programming languages like VHDL or Verilog.

FPGA was initially introduced in 1984 by Xilinx Inc., however, it did not become popular until 1990s when the first generation of Actel’s antifuse based FPGAs were produced.

FPGA stacked up against GPU

Intel announces that they have acquired Altera, an American FPGA manufacturer, for $16.7 billion. Intel plans to begin using their FPGAs in cloud computing to transition to an FPGA plus CPU architecture. Intel hopes that by integrating FPGAs into their multi-core server processors, the load will decrease and cloud computing speeds will increase.

Energy Consumption

A field-programmable gate array (FPGA) provides several advantages over a graphics processing unit (GPU). Unlike a GPU, an FPGA is designed to perform a specific task with high efficiency and requires the hardware pertaining to that task alone. [The bolded content is implied information that the reader can combine with the main text.

Cost and Programming time

FPGA is a more expensive option than GPU. FPGA is part of an ASIC (Application Specific Integrated Chip) that is manufactured at a high cost. Moreover, it takes a lot of time to hardwire it since that requires physical connections between units. In case of a GPU, this process takes a few minutes.

Latency

Latency describes the time delay of a certain action. In the case of an FPGA, this latency can be one millisecond. In the case of a GPU, latency is higher—as much as 50 milliseconds. Therefore, they are most effective when time is a critical factor—such as in Missile guidance systems and stock trading exchanges.

FPGAs don’t need to be attached to a host computer in order to run. This means that data doesn’t need to travel through multiple layers in order to reach them. FPGAs can therefore connect directly to input/output (I/O) sensors, reducing latency. A GPU, on the other hand, can’t function independently of a host computer–transmission of data to a GPU must go through proper channels (e.g., a PCI bus), thereby increasing latency.

Higher Bandwidth

FPGA is a more expensive option than GPU. FPGA is made using an ASIC, whereas GPU is made using a standard integrated circuit. In case of FPGA, it takes a lot of time to hardwire (program) the device since that requires physical connections between logical units. In case of GPU, this process takes only minutes.

If money is not your primary concern and you need to produce a result in a short time, then FPGA may be a good option for you.