Brainworks Foundry, Inc.

Markus Krause

Todd Allen Stiers

Phillip Alvelda, Vii

Zvi Huber

Aleksandra Joanna Sokolowska

The present disclosure relates to systems and methods to implement efficient high-bandwidth shared memory systems particularly suited for parallelizing and operating large scale machine learning and AI computing systems necessary to efficiently process high volume data sets and streams.

Efficient high bandwidth shared memory architectures for parallel machine learning and AI processing of large data sets and streams

Systems and methods for fully-automated contact-less biometric measurement include receiving a stream of image frames from an internet capable device. Frame data is extracted from the stream of image frames. A predictive load balancer selects a worker server based on a load prediction. An interface delivery server communicates the frame data from the internet capable device to the worker server. A set of patient face images captured by the frame data are extracted. A facial recognition machine learning model determines a patient identity associated with the set of patient face images of the frame data. A shared memory system stores the set of patient face images. The worker server determines biometric measurements based on the set of patient face images in the shared memory system using independent biometric data processing pipelines with shared access to the shared memory system for inter-process communication.

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Oakland, CA, United States of America

Markus Krause