What is FPGA?

Field Programmable Gate Arrays (FPGAs) are semiconductor devices that are based around a matrix of configurable logic blocks (CLBs) connected via programmable interconnects. FPGAs can be reprogrammed to desired application or functionality requirements after manufacturing.

The acceleration methods for neural network are mainly divided into two types:

Software design optimization goal is to reduce the computation or bandwidth requirements of the neural network. Again, there are three different ways:

The optimization of algorithm is a kind of procedure for different neural network models and the calculation processes is transformed without any affect in the result.

Data Quantification is used to reduce the bandwidth and storage requirements in neural network computing.

Weight reduction is to use a low rank matrix so that the actual weight is reduced by reducing the total calculation of the model.

Utilizing FPGA design accelerators for specific problems is currently the most extensive area of FPGA accelerator applications. Designing an accelerator specifically for a specific problem, it not only fits the problem well but also has a relatively small design difficulty. Designing accelerators for specific problems often speed up the reasoning process of deeplearning algorithms rather than the learning process.

Some of the advantages of FPGA based accelerator are its high performance even with low energy. Also, high parallelism is the main property of choosing FPGA platform.

FPGA can be applied to complex engineering situation due to the reconfiguability.

On the other hand the cost of reconfiguration is one of its dimerit.

Also, it requires hardware programming generally using hardware programming languages that would cost programmers much time to master.

Without a doubtAccelerating deep learning algorithms is a study that has increased many attentions in recent years. Although FPGA/ASIC also has such a good acceleration capability, it is only popularized in the research field due to programming complexity and other issues

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