At ISCA, we are revealing to the world our revolutionary technology that performs A.I. using Cellular Automata.

It will be introduced at the Workshop (New Approaches for Addressing the Computing Requirements of LLMs and GNNs) on Sunday June 30, at 1:55 at Breakout Room Quebracho B, in a session titled “Casting off the Old Guard: Achieving Superior A.I. Performance through Simplification”

Don’t miss this opportunity to witness a transformative leap in A.I. technology and learn how our architecture is poised to revolutionize the field. Look for us at ISCA!

Reserve time on our schedule below, if you miss the presentation.


White Papers

The BRAIN-CA™ Estimator and The Cincinnati Algorithm: Simplification Tools for Artificial Intelligence
Abstract: Learning is a process of creating a model of information as it is observed. The ideal storage format of such a model would represent learned information so that the model could be rapidly applied or modified, as additional observations are made. The BRAIN-CA™ Estimator is an elemental tool which enables this capability: the storage of a simple model in a manner that is accessible for rapid modification (learning) and rapid application (inference). A process called The Cincinnati Algorithm provides a simple method to update the Estimator model as additional observations are made.

Casting off the Old Guard: Achieving Superior A.I. Performance through Simplification
Training and deployment of huge machine learning models requires a vast amount of compute resources, and still dramatically underperforms the biological brain, which operates with six orders of magnitude less energy consumption. The trend of the conventional approach is toward more complicated engineered systems. At Brain-CA Technologies, we have designed a new architecture based on Cellular Automata. This simplified architecture casts off the old guard technologies, eliminating many of their inherent  restrictions. Our projections show that this new architecture provides significant improvement in energy consumption, while performing AI/ML tasks equivalently or better than existing systems.

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