Development of hardware accelerators on FPGA for convolutional neural networks

The aim of this thesis is to develop an architecture to accelerate the operation of convolution performed by neural networks. In particular it is to implement a matrix-matrix multiplication by executing the equation "C = A*B*alpha + C*beta".