Abstract: A systolic array of processing elements is connected to receive weight inputs and multiplexed data inputs for operation in two dimension convolution mode, or fully-connected neural network mode, or in cooperative, competitive neural network mode. Feature vector or two-dimensional image data is retrieved from external data memory and is transformed via input look-up table to input data for the systolic array. The convoluted image or outputs from the systolic array are scaled and transformed via output look-up table for storage in the external data memory. The architecture of the system allows it to calculate convolutions of any size within the same physical systolic array, merely by adjusting the programs that control the data flow.

Abstract: A systolic array of processing elements is connected to receive weight inputs and multiplexed data inputs for operation in feedforward, partially-- or fully-connected neural network mode or in cooperative, competitive neural network mode. Feature vector or two-dimensional image data is retrieved from external data memory and is transformed via input look-up table to input data for the systolic array that performs a convolution with kernal values as weight inputs. The convoluted image or neuron outputs from the systolic array are scaled and transformed via output look-up table for storage in the external data memory.