A method for data analysis according to an embodiment includes acquiring, from a client device, a ciphertext for a precomputation result generated by applying some of a plurality of operations for performing an analysis algorithm based on target data to the target data, and generating an encrypted computation result for remaining operations of the plurality of operations by using the ciphertext.

A method for data analysis according to an embodiment includes acquiring, from a client device, a ciphertext for a precomputation result generated by applying some of a plurality of operations for performing an analysis algorithm based on target data to the target data, and generating an encrypted computation result for remaining operations of the plurality of operations by using the ciphertext.

A representative reconfigurable processing circuit and a reconfigurable arithmetic circuit are disclosed, each of which may include input reordering queues; a multiplier shifter and combiner network coupled to the input reordering queues; an accumulator circuit; and a control logic circuit, along with a processor and various interconnection networks. A representative reconfigurable arithmetic circuit has a plurality of operating modes, such as floating point and integer arithmetic modes, logical manipulation modes, Boolean logic, shift, rotate, conditional operations, and format conversion, and is configurable for a wide variety of multiplication modes. Dedicated routing connecting multiplier adder trees allows multiple reconfigurable arithmetic circuits to be reconfigurably combined, in pair or quad configurations, for larger adders, complex multiplies and general sum of products use, for example.

A representative reconfigurable processing circuit and a reconfigurable arithmetic circuit are disclosed, each of which may include input reordering queues; a multiplier shifter and combiner network coupled to the input reordering queues; an accumulator circuit; and a control logic circuit, along with a processor and various interconnection networks. A representative reconfigurable arithmetic circuit has a plurality of operating modes, such as floating point and integer arithmetic modes, logical manipulation modes, Boolean logic, shift, rotate, conditional operations, and format conversion, and is configurable for a wide variety of multiplication modes. Dedicated routing connecting multiplier adder trees allows multiple reconfigurable arithmetic circuits to be reconfigurably combined, in pair or quad configurations, for larger adders, complex multiplies and general sum of products use, for example.

A multiply-accumulate (MAC) computation circuit includes: a source bit cell block configured to determine a MAC operation result of an input signal based on a plurality of source bit cells; a replica bit cell block comprising a plurality of replica bit cells corresponding to the plurality of source bit cells; and a readout circuit configured to read out a digital value of the MAC operation result using the replica bit cell block.

A multiply-accumulate (MAC) computation circuit includes: a source bit cell block configured to determine a MAC operation result of an input signal based on a plurality of source bit cells; a replica bit cell block comprising a plurality of replica bit cells corresponding to the plurality of source bit cells; and a readout circuit configured to read out a digital value of the MAC operation result using the replica bit cell block.

A method is presented for computing an equilibrium distribution of Markov processes. The method includes storing weight values in an analog crossbar array of transition probability matrices, where the analog crossbar array of transition probability matrices represents a weight matrix with m rows and n columns, computing an eigenvector associated with a real eigenvalue of modulus one for each of the transition probability matrices, applying a gradient-based eigenvalue solver to converge to a dominant eigenpair, and determining a probability of changing from one state to another state in a stochastic entity based on outcomes of the gradient-based eigenvalue solver.

A method is presented for computing an equilibrium distribution of Markov processes. The method includes storing weight values in an analog crossbar array of transition probability matrices, where the analog crossbar array of transition probability matrices represents a weight matrix with m rows and n columns, computing an eigenvector associated with a real eigenvalue of modulus one for each of the transition probability matrices, applying a gradient-based eigenvalue solver to converge to a dominant eigenpair, and determining a probability of changing from one state to another state in a stochastic entity based on outcomes of the gradient-based eigenvalue solver.

A method for neural network convolution, the method may include receiving input data that is a 3D input data and comprises input data segments associated with different input data depth values; receiving a convolution kernel that is a 3D convolution kernel and comprises kernel segments associated with different kernel depth values; performing multiple 3D convolution iteration, wherein each of 3D convolution iteration comprises: determining whether the 3D convolution iteration is of a first type or of a second type; executing the 3D convolution iteration of the first type when determining that the 3D convolution iteration is of the first type; and executing the 3D convolution iteration of the second type when determining that the 3D convolution iteration is of the second type.

A method for neural network convolution, the method may include receiving input data that is a 3D input data and comprises input data segments associated with different input data depth values; receiving a convolution kernel that is a 3D convolution kernel and comprises kernel segments associated with different kernel depth values; performing multiple 3D convolution iteration, wherein each of 3D convolution iteration comprises: determining whether the 3D convolution iteration is of a first type or of a second type; executing the 3D convolution iteration of the first type when determining that the 3D convolution iteration is of the first type; and executing the 3D convolution iteration of the second type when determining that the 3D convolution iteration is of the second type.