FPGA vs GPU For Deep Learning
Consider operational needs, budget, and goals when choosing a GPU or FPGA for a deep learning system
GPUs are a kind of specialised circuit designed to operate memory quickly in order to speed up the process of creating images
Applications such as deep learning and high performance computing (HPC) can be handled with ease by powerful GPUs
GPUs need a lot of power to run, which can have an effect on the environment and raise operational costs
FPGAs are versatile, especially in low-latency, specialised applications, unlike application-specific integrated circuits (ASICs)
Compared to conventional processors, FPGAs consume less power, which lowers operating expenses and has a positive environmental impact
By definition, deep learning applications entail building deep neural networks (DNNs), which are neural networks with three or more layers
Neural networks create decisions by recognising events, evaluating options, and drawing conclusions like biological neurons.
Due to its flexible programmability, low latency, and power efficiency, FPGAs are frequently utilised
General purpose GPUs aid applications that need more processing power and preprogramming
GPUs are built for rapid parallel computing, so they shorten large-scale deep learning model training times
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