Abstract: A system, method, and computer readable medium for deploying neural networks in low bit environments. The system comprises a runtime platform, a first set of configuration parameters identifying limitations of the runtime platform, and a quantization platform for quantizing neural networks. The quantization platform receives a neural network associated with a framework and quantizing the neural network into a smaller neural network and generates a dataset comprising a second set of configuration parameters for compiling the smaller neural network into instructions for the runtime platform. The second set of configuration parameters are responsive to the limitations of the first set of configuration parameters. The runtime environment implements the smaller neural network in accordance with the second set of configuration parameters.

Abstract: A system, method and computer readable medium for implementing neural networks. The method can include providing a neural network, providing a lookup table based on the neural network, packing weights and activations of the neural network associated with the at least one convolution with a first set of bitwise operations, unpacking the packed weights and activations, with a second set of bitwise operations, to determine one or more inputs for the look up table. The method includes accessing, within the lookup table, an output corresponding to the one or more inputs, and implement the at least one convolution based on the output.

Abstract: A system, device and method are provided for generating image processing models for selected hardware. The method, illustratively, includes obtaining a reference model, a desired image resolution based on target hardware, and a training set of images comprising images with the desired image resolution and images with a higher resolution. The method includes generating an updated model by: iteratively training the reference model with a combined set of features, the combined set of features comprising features determined from the images with the higher resolution with at least one stem and features determined from the images with the desired resolution. The method includes outputting the trained updated model to the target hardware to process images with the desired image resolution.

Abstract: A system, device and method are provided for reducing machine learning models for target hardware. Illustratively, the method includes providing a model, a set of training data, and a training threshold. A search space for reducing the model is determined with a pruning function and a pruning factor. The pruning function is bounded with constraints. Based on the constraints, boundaries for the pruning factor are determined, which boundaries define at least in part the search space. The pruning function increases compression along a depth of the model, and the compression increases are based on the pruning factor. A model is trained into a reduced model by iteratively updating model parameters based on the pruning function and the pruning factor and within the search space, and evaluating the updated model with the training parameters. The method includes providing the reduced model to target hardware.

Abstract: A system, method and computer readable medium are provided for implementing data free neural network pruning. The illustrative method include determining mutual information between outputs of two or more of the plurality neurons and a respective two or more inputs used to generate the outputs, the two or more neurons being activated as a result of synthetically created inputs for measuring entropy. The method includes determining a sparser neural network by pruning the plurality of neurons based on the determined mutual information.

Abstract: There is provided a system and method of automated design space determination for deep neural networks. The method includes obtaining a teacher model and one or more constraints associated with an application and/or target device or process used in the application configured to utilize a deep neural network; learning an optimal architecture using the teacher model, constraints, a training data set, and a validation data set; and deploying the optimal architecture on the target device or process for use in the application.

Abstract: There is provided a system and method of automated precision configuration for deep neural networks. The method includes obtaining an input model and one or more constraints associated with an application and/or target device or process used in the application configured to utilize a deep neural network; learning an optimal low-precision configuration of the architecture using constraints, the training data set, and the validation data set; and deploying the optimal configuration on the target device or process for use in the application.