A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.

A method for performing a matrix multiplication operation being secure against side-channel attacks according to one embodiment, which is performed by a computing device comprising one or more processors and a memory storing one or more programs to be executed by the one or more processors, includes shuffling an order of execution of multiplication operations between elements of a first matrix and elements of a second matrix for a matrix multiplication operation between the first matrix and the second matrix; and performing the matrix multiplication operation based on the shuffled order of execution.

A method for performing a matrix multiplication operation being secure against side-channel attacks according to one embodiment, which is performed by a computing device comprising one or more processors and a memory storing one or more programs to be executed by the one or more processors, includes shuffling an order of execution of multiplication operations between elements of a first matrix and elements of a second matrix for a matrix multiplication operation between the first matrix and the second matrix; and performing the matrix multiplication operation based on the shuffled order of execution.

An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.

Certain embodiments involve techniques for automatically identifying sounds in an audio recording that match a selected sound. An audio search and editing system receives the audio recording and preprocesses the audio recording into audio portions. The audio portions are provided as a query to the neural network that includes a trained embedding model used to analyze the audio portions in view of the selected sound to estimate feature vectors. The audio search and editing system compares the feature vectors for the audio portions against the feature vector for the selected sound and the feature vector for the negative samples to generate an audio score that is a numerical representation of the level of similarity between the audio portion and the selected sound and uses the audio scores to classify the audio portions into a first class of matching sounds and a second class of non-matching sounds.

Systems, apparatuses and methods may provide for technology that includes transposition hardware and a data controller coupled to the transposition hardware, the data controller to detect an input instruction, transfer, based on the input instruction, stored matrix data from a memory to the transposition hardware, and configure the transposition hardware to stream output transposed matrix data associated with the stored matrix data.

Systems, apparatuses and methods may provide for technology that includes transposition hardware and a data controller coupled to the transposition hardware, the data controller to detect an input instruction, transfer, based on the input instruction, stored matrix data from a memory to the transposition hardware, and configure the transposition hardware to stream output transposed matrix data associated with the stored matrix data.

Numerous embodiments of a precision programming algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Numerous embodiments of a precision programming algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.