Abstract: An accelerator for training deep neural networks is provided. The accelerator includes a baseline architecture having an input buffer, a weight buffer, an output buffer, a buffer controller, and a two-dimensional array of processing elements. The array of processing elements is used in both convolutional and fully connected layers. The convolutional layer includes multiple filters. The output of each said filter in said convolutional layers is achieved by a weighted summation. In a preferred embodiment, each convolutional and fully connected layer is equipped with input/output buffers that fetch/store the input/output data. In a particularly preferred embodiment, each processing element can access the weight buffer that holds the weight vector.