The main difference between the two is that FPGAs are programmable and can be used for a variety of tasks, while GPUs are specialized for graphics-related tasks. In terms of performance, FPGAs are generally faster than GPUs. However, GPUs are generally more power efficient.
Related article: Difference Between FPGA and CPLD
What is FPGA?
FPGA stands for field programmable gate array. FPGA is a programmable logic device that harbors a complex architecture that allows them a high logic capacity, making them ideal for high gate count designs such as server applications, and video encoders/decoders. Due to the fact of FPGA consist of a large number of gates the internal delay in this chip is sometimes unpredictable.
What is GPU?
GPU stands for graphics processing unit, It is a processor specially designed for computing graphical displays. It is typically incorporated with the CPU for sharing RAM with the CPU which is good and easy work for most computing tasks. It is needed for high-end graphics-intensive processing. The separate GPU unit has its own RAM, sometimes referred to as video RAM or VRAM.
FPGA Vs GPU | Difference between FPGA and GPU:
- FPGA stands for field programmable gate array, and GPU stands for the graphics processing unit.
- FPGAs offer low latency than GPUs, While GPUs have relatively high latency than FPGAs.
- FPGA is a better energy-efficient solution compared to GPU. While GPUs are similarly power-efficient, SIMD streams are the only ones.
- FPGA is not good at floating-point computation. GPUs are excellent at performing floating-point operations.
- FPGA are difficult to program. While GPU has a mature ecosystem.
- Hardware description languages or HDL, such as VHDL and Verilog can be used to program FPGAs. programming languages for general-purpose software such as C, C++, Java, Python, etc, can be used to set up GPUs.
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