Brain-CA: A Cellular Automata-Inspired Architecture for Energy-Efficient CPU Design
ABSTRACT Data centers face a growing energy crisis, driven by the demands of AI [...]
Brain-CA: A Dual-Speed, Low-Energy Architecture for Cognitive Learning via Cellular Automata
ABSTRACT Brain-CA addresses the growing energy demands of AI—especially in data centers reliant on [...]
A Uniform, Tessellated Architecture for Energy-Efficient Learning and Inference
Abstract—This paper presents a novel architecture designed from first principles to support real-time learning and [...]
The BRAIN-CA™ Estimator and Cincinnati Algorithm: Cornerstone of Teleomorphic Computing
Introduction In the rapidly evolving landscape of artificial intelligence, a groundbreaking approach is emerging [...]
Inventing the Estimator and the Brain-CA Learning System: A New Path in AI
This paper introduces the Estimator and the Brain-CA Learning System, explaining how these inventions create a scalable and efficient path for AI learning.
The Search for an Elemental AI Device: The Emergence of the Estimator
Introduction Artificial Intelligence (AI) has achieved unprecedented breakthroughs in recent decades, largely powered by [...]
A Stochastic Approach to Energy-Efficient Machine Learning
Introduction Conventional neural network models used for machine learning tasks typically employ a brute-force [...]
What is Teleomorphic Computing?
Introduction In the ever-evolving landscape of computing paradigms, a new approach has emerged that [...]
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, [...]
The BRAIN-CA™ Estimator and The Cincinnati Algorithm: Simplification Tools for Artificial Intelligence
Learning is a process of creating a model of information as it is observed. The [...]
