AI Project

Brain inspired

New brain-inspired and massively-scalable computing methodologies for novel machine learning approaches that have the potential to support contextual adaptations to real-world dynamic intelligence problems.

Neural Network

This improved with the second wave of AI where statistical models such as deep-learning neural networks scored high marks with learning and perceiving, however at the expense of large data sets and much training.

Shared Learning

The next generation AI, the third wave, will have better perception and reasoning along with abstracting and learning to deal with contextual adaptions. Shared learning algorithms have the ability to use previous training to predict future outcomes. 

Power Optimization

Brain-inspired algorithms are developed to meet the challenges such as noise, algorithm performance, accuracy, robustness, quality, data size, and power consumption. Optimization of hardware and software computing results in greater power efficiency. 


Human Memory, Hardware FPGA, Brain-Inspired DARPA Research, Autonomous Drones


Within this large brain network are 100 trillion synapses, 100 billion neurons, in total consuming only 20 watts of energy. In order to scale to this level of computing power, the next generation of AI computing power needs to be highly efficient.

Accelerate next generation of AI research by exploring under-explored mathematical algorithms from “brain-inspired” massively-scalable approaches.

Develop greater abstracting and reasoning capabilities such as applying knowledge from one domain to another domain along with perceiving and learning.

This model must validate for potential of 10x improvement in energy efficiency and data rate handling capability.


Performers will be expected to provide at a minimum the following deliverables: negotiated deliverables specific to the proposed effort. These may include reports; experimental and simulated data sets; proposed architectures; protocols; software codes; publications; model data; metrics; validation data; and other associated documentation and results.

Report on novel computational theory and related initial brain-inspired algorithms.

Report on updated expectations for energy efficiency improvements and data handling capabilities of the proposed approach and preliminary discussion of potential hardware implementation.

Report on initial training and test data sets (simulated or modeled), evaluation metrics and initial analyses results.

Trillion Synapses
Billion Neurons
Watts of Energy
Average Weight (g)

What Researchers Say

This research has the potential to reveal human memory mysteries and uncover brain related diseases such as Alzheimer's and dementia.

dr. brain

Application development of brain-inspired AI algorithms can be used in autonomous vehicles, visual odometry, and big data.

Jane Doe
software engineer

Intelligent Agents (IA) brain-inspired algorithms provides a framework for contextual recognition of the environment.

John Doe
AI Researcher

AI Links

© 2019 Voracity Co.
Powered by 

Translate »