With outstanding performance and power metrics, Xilinx FPGAs are the designer's first choice for building convolutional neural networks. New software tools simplify implementation.
Artificial intelligence is undergoing a revolution, thanks to the rapid advancement of machine learning. In the field of machine learning, there is a strong interest in a class of algorithms called "deep learning" because of their excellent big data set performance. In deep learning, machines can learn a task from a large amount of data in a supervised or unsupervised manner. Large-scale supervised learning has achieved great success in tasks such as image recognition and speech recognition.
Deep learning techniques use a large amount of known data to find a set of weights and bias values ​​to match the expected results. This process is called training and produces large patterns. This motivates engineers to use specialized hardware (such as GPUs) for training and classification.
As the amount of data increases, machine learning will shift to the cloud. The large machine learning mode is implemented on the CPU in the cloud. Although the GPU is a better choice for performance in terms of deep learning algorithms, the power requirements are high enough to be used in high performance computing clusters. Therefore, there is a need for a processing platform that can speed up the algorithm without significantly increasing power consumption. In this context, FPGAs seem to be an ideal choice, and their inherent features help to easily launch many parallel processes under low power conditions.
Let's take a closer look at how to implement Convolutional Neural Networks (CNN) on Xilinx FPGAs. CNN is a type of deep neural network that has been successful in dealing with large-scale image recognition tasks and other issues similar to machine learning. In the current case, do a feasibility study on implementing CNN on an FPGA to see if the FPGA is suitable for solving large-scale machine learning problems. Click to see more exciting content
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