Hrl Laboratories, LLC

Other

Karl Paul Dockendorf

Charles E Martin

Narayan Srinivasa

David W Payton

Dean C Mumme

A novel consumer grade automatic sorter for pieces is disclosed with an application to organizing interlocking toy brick systems. The invention of a novel means to propel and separate pieces, and process sensor data in combination with standard methods for binning and storage. A novel mechanical screw is proposed to propel the parts. The device comprises a novel combination of backgrounds and artificial intelligence/machine learning to enable sensing of colors, estimation of size, and transparency of pieces. In addition, a method to embed a neural network efficiently on a microcontroller is disclosed.

Method and apparatus for sorting pieces

Described is an inference circuit for pattern recognition for use within convolutional neural nets for online learning using K-means clustering. The inference circuit includes a set of templates, each template having a template data memory. The inference circuit also include at least one match element, the match element being operable for receiving an input data pattern and determining a degree of match between the input data pattern and a template. A best match logic circuit is included for selecting a template in the set of templates that best matches the input data pattern, said template being a best match template. Finally, an updated is included for probabilistically modifying the template data memories based on the best match template.

Inference circuit for improving online learning

Described is a plastic hyper-dimensional memory system having neuronal layers. The system includes an input layer for receiving an input and an address matrix for generating a working pre-image vector from the input. A hidden layer is included for transforming the working pre-image vector into a working vector. A data matrix transforms the working vector into a data pre-image vector. Further, the hidden layer performs neurogenesis when a novel input is detected based on the working pre-image vector, where the neurogenesis comprises adding or deleting address units. Novelty detection includes using a set of reinforcement units. Finally, an output layer generates a data vector based on the data pre-image vector.

Plastic hyper-dimensional memory

Described is a system for adapting neural networks. The system receives inputs to be learned by a multi-layered spiking neural network. A first mechanism is implemented to adapt weights on the connections via competition among neurons using Hebbian learning. Activity levels of the neurons are stabilized to allow the multi-layered spiking neural network to learn the inputs. A second mechanism is implemented to increase a learning rate of a neuron over time using Hebbian learning. A third mechanism is implemented, wherein newly created neurons, representing new inputs, copy at least one synaptic structure of older neurons in the multi-layered spiking neural network. The mechanisms are used for continuous, online learning of the inputs to the multi-layered spiking neural network. An autonomous system, such as an autonomous vehicle, can use the learned inputs to learn from its environment and perform tasks, such as classification and prediction.

Plastic neural networks

Described is a system for learning, prediction, and recall of spatiotemporal patterns. An input spatiotemporal sequence is learned using a recurrent spiking neural network by first processing the input spatiotemporal sequence using the recurrent spiking neural network. The recurrent spiking neural network comprises neurons having excitatory synaptic connections and inhibitory synaptic connections. Balanced inhibitory connectivity exists between neurons having excitatory synaptic connections. The recurrent spiking neural network uses distinct forms of synaptic plasticity for excitatory synaptic connections and inhibitory synaptic connections, such that excitatory synaptic connections strengthen and inhibitory synaptic connections weaken. In another aspect, the system is able to recall the learned spatiotemporal sequence and predict a future spatiotemporal sequence through activation of the recurrent spiking neural network.

Growing community of inventors

Jacksonville, FL, United States of America

Karl Paul Dockendorf