Abstract: Methods, systems, and computer program products for accelerating inference of transformer-based models are provided herein. A computer-implemented method includes obtaining a machine learning model comprising a plurality of transformer blocks, a task, and a natural language dataset; generating a compressed version of the machine learning model based on the task and the natural language dataset, wherein the generating comprises: obtaining at least one set of tokens, wherein each token in the set corresponds to one of the items in the natural language dataset, identifying and removing one or more redundant output activations of different ones of the plurality of transformer blocks for the at least one set of tokens, and adding one or more input activations corresponding to the one or more removed output activations into the machine learning model at subsequent ones of the plurality of the transformer blocks; and outputting the compressed version of the machine learning model to at least one user.

Abstract: Methods, systems, and computer program products for accelerating inference of transformer-based models are provided herein. A computer-implemented method includes obtaining a machine learning model comprising a plurality of transformer blocks, a task, and a natural language dataset; generating a compressed version of the machine learning model based on the task and the natural language dataset, wherein the generating comprises: obtaining at least one set of tokens, wherein each token in the set corresponds to one of the items in the natural language dataset, identifying and removing one or more redundant output activations of different ones of the plurality of transformer blocks for the at least one set of tokens, and adding one or more input activations corresponding to the one or more removed output activations into the machine learning model at subsequent ones of the plurality of the transformer blocks; and outputting the compressed version of the machine learning model to at least one user.

Abstract: One or more computer processors decompose a weight matrix associated with a neural network utilizing a permutation dependent decomposition. The one or more computer processors regenerate a recovered matrix utilizing the decomposed weight matrix. The one or more computer processors reduce an error between the decomposed weight matrix and regenerated recovered matrix.

Abstract: One embodiment provides a computer implemented method, including: receiving an application including a plurality of services to be hosted on a remote services environment; obtaining, a resource profile identifying a usage of a resource by a given service of the plurality of services over a period of time; splitting, based upon the resource profile corresponding to a given service, the given service into a plurality of service slices; selecting, for each of the plurality of service slices an incarnation fulfilling a resource demand requirement and a service-performance-offering, wherein an incarnation has a total demand value based upon a resource capacity of a node at the remote services environment; and assigning, for each of the plurality of service slices the incarnation selected to at least one node within the remote services environment based upon the resource capacity of the at least one node.

Abstract: Methods, systems, and computer program products for dataset dependent low rank decomposition of neural networks are provided herein. A computer-implemented method includes obtaining a target dataset and a trained model of a neural network; providing at least a portion of the target dataset to the trained model; determining relevance of each of one or more of filters of the neural network and channels of the neural network to the target dataset based on the provided portion, wherein the one or more of the filters and the channels correspond to at least one layer of the neural network; and compressing the trained model of the neural network based at least in part on the determined relevancies.

Abstract: Described herein are methods, systems, apparatuses and products for phase determination in an electricity grid. An aspect provides for determining phase for at least one electric grid device via receiving power consumption measurements derived from electricity grid devices connected in the electricity grid, comparing, over a series of time intervals, a total power consumed; and determining a phase for at least one electricity grid device.

Abstract: Described herein are methods, systems, apparatuses and products for phase determination in an electricity grid. An aspect provides for determining phase for at least one electric grid device via receiving power consumption measurements derived from electricity grid devices connected in the electricity grid, comparing, over a series of time intervals, a total power consumed; and determining a phase for at least one electricity grid device.