International Business Machines Corporation

Eth Zurich (eidgenoessische Technische Hochschule Zurich)

Eth Zurich

Kumudu Geethan Karunaratne

Giovanni Cherubini

Abu Sebastian

Manuel Le Gallo-Bourdeau

Abbas Rahimi

Luca Benini

The present disclosure relates to a method for classifying a query information element using the similarity between the query information element and a set of support information elements. A resulting set of similarity scores is transformed using a sharpening function such that the transformed scores are decreasing as negative similarity scores increase and the transformed scores are increasing as positive similarity scores increase. A class of the query information element is determined based on the transformed similarity scores.

Memory-augmented neural network system

The present disclosure relates to a method for representing an ordered group of symbols with a hypervector. The method comprises sequentially applying on at least part of the input hypervector associated with a current symbol a predefined number of circular shift operations associated with the current symbol, resulting in a shifted hypervector. A rotate operation may be applied on the shifted hypervector, resulting in an output hypervector. If the current symbol is not the last symbol of the ordered group of symbols the output hypervector may be provided as the input hypervector associated with a subsequent symbol of the current symbol; otherwise, the output hypervector of the last symbol of the ordered group of symbols may be provided as a hypervector that represents the ordered group of symbols.

Representation of an ordered group of symbols by hypervectors

A system for hyper-dimensional computing for inference tasks may be provided. The device comprises an item memory for storing hyper-dimensional item vectors, a query transformation unit connected to the item memory, the query transformation unit being adapted for forming a hyper-dimensional query vector from a query input and hyper-dimensional base vectors stored in the item memory, and an associative memory adapted for storing a plurality of hyper-dimensional profile vectors and for determining a distance between the hyper-dimensional query vector and the plurality of hyper-dimensional profile vectors, wherein the item memory and the associative memory are adapted for in-memory computing using memristive devices.

Device for hyper-dimensional computing tasks

The invention is directed to a device for high-dimensional encoding of a plurality of sequences of quantitative data signals. The device comprises a plurality of input channels for receiving the plurality of sequences of quantitative data signals and an encoding unit. The encoding unit is configured to perform a temporal high-dimensional encoding of n-grams of the plurality of sequences of quantitative data signals; thereby creating a plurality of temporally encoded hypervectors for the plurality of input channels. The encoding unit is further configured to perform a spatial high-dimensional encoding of the plurality of temporally encoded hypervectors, thereby creating a temporally and spatially encoded hypervector. The device further comprises a configuration controller. The configuration controller is adapted to configure the high-dimensional encoding in dependence on one or more hyperparameter values.

Device for high dimensional encoding

The invention is notably directed at a device for high-dimensional computing comprising an associative memory module. The associative memory module comprises one or more planar crossbar arrays. The one or more planar crossbar arrays comprise a plurality of resistive memory elements. The device is configured to program profile vector elements of profile hypervectors as conductance states of the resistive memory elements and to apply query vector elements of query hypervectors as read voltages to the one or more crossbar arrays. The device is further configured to perform a distance computation between the profile hypervectors and the query hypervectors by measuring output current signals of the one or more crossbar arrays. The invention further concerns a related method and a related computer program product.

Device for high dimensional computing comprising an associative memory module

The invention is directed a device for high-dimensional encoding of a plurality of sequences of quantitative data signals. The device comprises a memory crossbar array comprising a plurality of resistive devices, a first peripheral circuit connected to the memory crossbar array, and a second peripheral circuit connected to the first peripheral circuit. The device is configured to receive the plurality of sequences of quantitative data signals via a plurality of input channels and to store elements of a plurality of precomputed basis hypervectors as conductance states of the resistive devices. The plurality of basis hypervectors are bound to respective input channels. The first peripheral circuit performs a temporal encoding of n-grams of the quantitative data signals thereby creating a plurality of temporally encoded hypervectors. The second peripheral circuit performs a spatial encoding of the plurality of temporally encoded hypervectors. This creates a temporally and spatially encoded hypervector.

The device provides a resistive memory device for storing elements of hyper-dimensional vectors, in particular digital hyper-dimensional, as conductive statuses in components in particular in 2D-memristors, of the resistive memory device, wherein the resistive memory device provides a first crossbar array of the components, wherein the components are memristive 2D components addressable by word-lines and bit-lines, and a peripheral circuit connected to the word-lines and bit-lines and adapted for encoding operations by activating the word-lines and bit-lines sequentially in a predefined manner.

Growing community of inventors

Gattikon, Switzerland

Kumudu Geethan Karunaratne