Abstract: This invention is in the field of machine learning and neural associative memory. In particular the invention discloses a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for storing and retrieving such associations. Bayesian learning is applied to achieve non-linear learning.

Abstract: This invention is in the field of machine learning and neural associative memory. In particular the invention discloses a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for retrieving such associations. A method for a non-linear synaptic learning of discrete synapses is disclosed, and its application on neural networks is laid out.

Abstract: A method for forming an associative computer memory comprises the step of forming an inhibitory memory matrix A?=?(Ap?A). According to the Wilshaw model constructed from a given set of address patterns and content patterns' and random matrix structure.

Abstract: A memory device comprising at least one memory stack of stacked memory dies which are staggered with respect to each other, each stacked memory die of said memory stack comprising along its edge die pads for bonding said stacked memory die to substrate pads of said memory device connectable to a control circuit, wherein each die pad of a stacked memory die which connects said memory die individually to said control circuit comprises an increased distance (di) in comparison to die pads of said stacked memory die which connect said stacked memory die in parallel with corresponding die pads of other stacked memory dies of said memory stack to said control circuit.

Abstract: This invention is in the field of machine learning and neural associative memory. In particular the invention discloses a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for storing and retrieving such associations. Bayesian learning is applied to achieve non-linear learning.

Abstract: This invention is in the field of machine learning and neural associative memory. In particular the invention discloses a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for retrieving such associations. A method for a non-linear synaptic learning of discrete synapses is disclosed, and its application on neural networks is laid out.

Abstract: A method for forming an associative computer memory comprises the step of forming an inhibitory memory matrix A?=?(Ap?A). According to the Wilshaw model constructed from a given set of address patterns and content patterns' and random matrix structure.

Abstract: A memory device comprising at least one memory stack of stacked memory dies which are staggered with respect to each other, each stacked memory die of said memory stack comprising along its edge die pads for bonding said stacked memory die to substrate pads of said memory device connectable to a control circuit, wherein each die pad of a stacked memory die which connects said memory die individually to said control circuit comprises an increased distance (di) in comparison to die pads of said stacked memory die which connect said stacked memory die in parallel with corresponding die pads of other stacked memory dies of said memory stack to said control circuit.