Abstract: A processor-implemented method includes bit shifting a binary representation of a neural network parameter. The neural network parameter has fewer bits, b, than a number of hardware bits, B, supported by hardware that processes the neural network parameter. The bit shifting effectively multiplies the neural network parameter by 2B-b. The method also includes dividing a quantization scale by 2B-b to obtain an updated quantization scale. The method further includes quantizing the bit shifted binary representation with the updated quantization scale to obtain a value for the neural network parameter.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In one aspect, base model output data is generated, the generating including processing input data with at least a portion of a base model of a machine learning model architecture, and the base model output data is processed with a routing model of the machine learning model architecture in order to determine a selected expert model, of a plurality of expert models, with which to process the base model output data. Expert model output data is generated, where generating the expert model output data includes processing the base model output data with the selected expert model, and final output data from the machine learning model architecture is generated, where generating the final output data includes processing the base model output data and the expert model output data with an ensemble model of the machine learning model architecture.

Abstract: Various embodiments include methods and devices for neural network pruning. Embodiments may include receiving as an input a weight tensor for a neural network, increasing a level of sparsity of the weight tensor generating a sparse weight tensor, updating the neural network using the sparse weight tensor generating an updated weight tensor, decreasing a level of sparsity of the updated weight tensor generating a dense weight tensor, increasing the level of sparsity of the dense weight tensor the dense weight tensor generating a final sparse weight tensor, and using the neural network with the final sparse weight tensor to generate inferences. Some embodiments may include increasing a level of sparsity of a first sparse weight tensor generating a second sparse weight tensor, updating the neural network using the second sparse weight tensor generating a second updated weight tensor, and decreasing the level of sparsity the second updated weight tensor.

Abstract: Various aspects provide methods for a computing device selecting a neural network for a hardware configuration including using an accuracy predictor to select from a search space a neural network including a first plurality of the blockwise knowledge distillation trained search blocks, in which the accuracy predictor is built using search space trained blockwise knowledge distillation search blocks. Aspects may include selecting a second plurality of the blockwise knowledge distillation trained search blocks based on criteria of predicted accuracy using the accuracy predictor for the second plurality of the blockwise knowledge distillation trained search blocks.