University of California

Tajana Rosing

Mohsen Imani

Farinaz Koushanfar

Sahand Salamat

Mohammad Sadegh Riazi

Yeseong Kim

Behnam Khaleghi

Saransh Gupta

A method of operating on encrypted data can be performed by receiving ciphertexts at a server that is configured to operate on the ciphertexts generated using a 3generation RGSW based fully homomorphic encryption system, operating on the ciphertexts received at the server in response to requested operations to generate respective input ciphertexts including ciphertext polynomials and ciphertext integers that are representative of the input ciphertexts, and processing the input ciphertexts in a server processing-in-memory device, that is operatively coupled to the server, to perform operations on the input ciphertext using the server processing-in-memory device, in-situ.

Methods of operating on data in a fully homomorphic encryption system using in-situ processing-in-memory and related circuits

A method of defining an implementation of circuits in a programmable device can be provided by receiving a plurality of specifications for a hyperdimensional (HD) computing machine learning application for execution on a programmable device, determining parameters for a template architecture for HD computing machine learning using the plurality of specifications, the template architecture including an HD hypervector encoder, an HD associative search unit, programmable device pre-defined processing units, and programmable device pre-defined processing elements within the pre-defined processing units, and generating programmable device code configured to specify resources to be allocated within the programmable device using pre-defined circuits defined for use in the programmable device using the determined parameters for the template architecture.

Methods and systems configured to specify resources for hyperdimensional computing implemented in programmable devices using a parameterized template for hyperdimensional computing

A computing system can include a plurality of clients located outside a cloud-based computing environment, where each of the clients may be configured to encode respective original data with a respective unique secret key to generate data hypervectors that encode the original data. A collaborative machine learning system can operate in the cloud-based computing environment and can be operatively coupled to the plurality of clients, where the collaborative machine learning system can be configured to operate on the data hypervectors that encode the original data to train a machine learning model operated by the collaborative machine learning system or to generate an inference from the machine learning model.

Systems, circuits and computer program products providing a framework for secured collaborative training using hyper-dimensional vector based data encoding/decoding and related methods

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San Diego, CA, United States of America

Tajana Rosing