Abstract: Methods and apparatus are disclosed supporting a design flow for developing quantized neural networks. In one example of the disclosed technology, a method includes quantizing a normal-precision floating-point neural network model into a quantized format. For example, the quantized format can be a block floating-point format, where two or more elements of tensors in the neural network share a common exponent. A set of test input is applied to a normal-precision flooding point model and the corresponding quantized model and the respective output tensors are compared. Based on this comparison, hyperparameters or other attributes of the neural networks can be adjusted. Further, quantization parameters determining the widths of data and selection of shared exponents for the block floating-point format can be selected. An adjusted, quantized neural network is retrained and programmed into a hardware accelerator.

Abstract: A computer implemented method of optimizing a neural network includes obtaining a deep neural network (DNN) trained with a training dataset, determining a spreading signal between neurons in multiple adjacent layers of the DNN wherein the spreading signal is an element-wise multiplication of input activations between the neurons in a first layer to neurons in a second next layer with a corresponding weight matrix of connections between such neurons, and determining neural entropies of respective connections between neurons by calculating an exponent of a volume of an area covered by the spreading signal. The DNN may be optimized based on the determined neural entropies between the neurons in the multiple adjacent layers.