The integrated circuits industry is rapidly changing with a lot of integrated circuits out there — CPU, GPU, FPGA, among others — and each one of them performs mathematical tasks. The only difference between the three is the way they perform the task at hand.
A GPU (Graphics Processing Unit) is a specialised instruction based computing chip. It performs mathematical computations for image processing and 3D image rendering and is very efficient at this task. In a way, a GPU is a CPU (Central Processing Unit) that is responsible for performing accurate calculations required for image processing.
An instruction-based machine breaks the task at hand into smaller instructions and executes the instructions. If you are familiar with coding, you write instructions for the CPU to run in a step-by-step manner. These are, in turn, used to perform a particular task. However, FPGA does not function this way
FPGA (Field Programmable Gate Array) is a reconfigurable integrated circuit and does not require instructions to perform a task at hand. Instead, basic logical blocks (AND gates, OR gates) are hardwired to complete a task. These wires which connect the various logic gates can be changed, which will modify the function of the FPGA.
Doing FPGA programming using instruction-based coding platforms like C++ or Java is not possible. They require something known as a Hardware Description Language like VHDL for configuration.
Also read: What is GPU scaling? Types and how to enable scaling?
FPGA stacked up against GPU
Intel acquired Altera, an American FPGA manufacturing company, for $16.7 billion. Intel is using FPGAs in cloud computing to transition to an FPGA plus CPU architecture. FPGAs would help decrease the load on Intels’ multi-core server processors, making cloud computing faster and safer.
As mentioned above, both FPGA and GPU are fundamentally different from one another. Here we will be talking about the four parameters that make them different.
Latency
Latency refers to the time delay in the execution of a task. In the case of an FPGA, this latency can be as low as one millisecond. In the case of a GPU, latency is higher and can go up to 50 milliseconds. Therefore, they are the ideal choice, where time is a very critical factor, such as Missile guidance systems and stock trading exchanges.
An FPGA does not require a host system to run and is self-sustainable. Due to this reason, data does not have to travel through multiple layers to reach the FPGA. So, FPGA connects directly to input/output sensors and decreases the latency. A GPU, on the other hand, cannot work without a host computer. Transmission of data to the GPU needs to go through proper channels (a PCI bus), thereby increasing the latency.
Energy Consumption
A very significant advantage that an FPGA provides is its low power consumption. An FPGA is designed to perform a specific task with high efficiency and requires the hardware pertaining to that task alone. A GPU, on the other hand, being an instructional computation device needs to run various computational blocks to perform a particular task. This makes a GPU a power-hungry device compared to an FPGA.
Higher Bandwidth
As FPGA can connect directly to input/output sensors, they offer higher bandwidths for data transmission. Therefore, FPGA is used with space radios that tend to send data in bulk quantities.
Also, FPGA is exceptionally suitable for doing a task over and over again, thus making them ideal for Bitcoin mining.
Cost and Programming time
FPGA costs more when compared to the GPU. FPGA is a part of ASIC (Application Specific Integrated Chips) that require a high initial cost for manufacturing. Moreover, it takes a lot of time to hardwire (program) an FPGA since that requires physical connections between logical units. In case of a GPU, this process takes a few minutes.
In conclusion, if money is not an issue and the task at hand is time-sensitive, then an FPGA is the option for you.
