Key takeaways
- The competition between GPUs and FPGAs is intensifying, with each offering distinct advantages for autonomous vehicle technology.
- GPUs excel in cost efficiency and parallel processing, while FPGAs provide flexibility and power efficiency for specific tasks.
- The rise of cloud computing and deep learning is prompting a broader adoption of GPUs in the industry.
- FPGAs require skilled design engineers, making them more challenging and costly to implement compared to GPUs.
We are back to the battle of graphics processing units vs field-programmable gate array (FPGA) with NVIDIA in the centre of attention. This time, there’s more at stake than just FPGA power efficiency factor as compared to GPUs cost efficient advantage. The recent tech boom with cloud computing, image processing, robotics, deep learning and big data workloads have necessitated a shift towards GPUs.
But that doesn’t rule out FPGAs which are designed to perform fixed-point operations with a close-to-hardware programming approach. GPUs on the other hand, are optimised for parallel processing of floating-point operations using thousands of small cores, according to this whitepaper. FPGAs also score over GPUs in terms of interface flexibility, improved by the integration of programmable logic with CPUs and standard peripherals. They also provides huge processing capabilities with a great power efficiency. But when it comes to cost efficiency, GPUs are winning the battle in development and software acceleration while FPGAs require a team of design engineers. Another thing to be considered is that, today many algorithms are designed directly for GPUs, and FPGA developers are difficult and expensive to hire.
