Architecture of a computer for calculating a convolution layer in a convolutional neural network

A computer for computing a convolutional layer of an artificial neural network, comprising:

• at least one set of at least two partial sum computing modules connected in series, a storage member for storing the coefficients of at least one convolution filter, each partial sum computing module comprising several computing units each configured so as to carry out a multiplication of an item of input data of the computer and a coefficient of a convolution filter, followed by an addition of the output of the preceding partial sum computing module in the series or of a predefined value for the first partial sum computing module in the series,
• each set furthermore comprising, for each partial sum computing module except the first in the series, a shift register connected at input for storing the item of input data for the processing duration of the preceding partial sum computing modules in the series, said shift register being connected at output to the next partial sum computing module in the series and being configured to introduce a latency equivalent to said processing duration to the data received by said next partial sum computing module in the series,
• the computer furthermore comprising at least one accumulator connected at output of each set and a memory, the input data of the computer coming from at least two input matrices, each partial sum computing module being configured so as to receive, at input, the input data belonging to different input matrices and having the same coordinates in each input matrix,
• in each clock cycle, a value of one of the at least two input matrices, read sequentially row by row, being received at input of one of the at least two partial sum computing modules, said value being received, in parallel, at input of each computing unit belonging to said one of the at least two partial sum computing modules, said computing units each receiving a coefficient of the convolution filter to implement a multiplication with said value in order to compute a different output neuron.