Sri International

Syracuse University

Sri Inlernational

Aswin Nadamuni Raghavan

Sek Meng Chai

Mohamed R Amer

David Chao Zhang

Timothy J Shields

Bhaskar S Ramamurthy

Ruby B Lee

Amir Tamrakar

Michael Raymond Piacentino

Zecheng He

Jesse Hostetler

Timothy J Meo

David A Salter

Senem Velipasalar

Alex C Tozzo

Kyung-Yoon Kim

Samyak Parajuli

Indranil Sur

Abrar Abdullah Rahman

Method and apparatus of processing a sequence of video frames comprising generating at least one video frame and using an analog neural network to select, within the at least one video frame, at least one patch of pixels and process the at least one pixel patch to produce a patch feature for each of the at least one pixel patches. The method digitizes the patch feature, identifies objects within the digitized patch feature, and tracks the objects to generate control information that is used by the analog neural network to select and process the pixel patches.

Video processor capable of in-pixel processing

Artificial neural network systems involve the receipt by a computing device of input data that defines a pattern to be recognized (such as faces, handwriting, and voices). The computing device may then decompose the input data into a first subband and a second subband, wherein the first and second subbands include different characterizing features of the pattern in the input data. The first and second subbands may then be fed into first and second neural networks being trained to recognize the pattern. Reductions in power expenditure, memory usage, and time taken, for example, allow resource-limited computing devices to perform functions they otherwise could not.

Low precision neural networks using subband decomposition

A low-cost, low-power, stand-alone sensor platform having a visible-range camera sensor, a thermopile array, a microphone, a motion sensor, and a microprocessor that is configured to perform occupancy detection and counting while preserving the privacy of occupants. The platform is programmed to extract shape/texture from images in spatial domain; motion from video in time domain; and audio features in frequency domain. Embedded binarized neural networks are used for efficient object of interest detection. The platform is also programmed with advanced fusion algorithms for multiple sensor modalities addressing dependent sensor observations. The platform may be deployed for (i) residential use in detecting occupants for autonomously controlling building systems, such as HVAC and lighting systems, to provide energy savings, (ii) security and surveillance, such as to detect loitering and surveil places of interest, (iii) analyzing customer behavior and flows, (iv) identifying high performing stores by retailers.

Low power and privacy preserving sensor platform for occupancy detection

Techniques are disclosed for training machine learning systems. An input device receives training data comprising pairs of training inputs and training labels. A generative memory assigns training inputs to each archetype task of a plurality of archetype tasks, each archetype task representative of a cluster of related tasks within a task space and assigns a skill to each archetype task. The generative memory generates, from each archetype task, auxiliary data comprising pairs of auxiliary inputs and auxiliary labels. A machine learning system trains a machine learning model to apply a skill assigned to an archetype task to training and auxiliary inputs assigned to the archetype task to obtain output labels corresponding to the training and auxiliary labels associated with the training and auxiliary inputs assigned to the archetype task to enable scalable learning to obtain labels for new tasks for which the machine learning model has not previously been trained.

Generative memory for lifelong machine learning

A method, apparatus and system for anomaly detection in a processor based system includes training a deep learning sequence prediction model using observed baseline behavioral sequences of at least one processor behavior of the processor based system, predicting baseline behavioral sequences from the observed baseline behavioral sequences using the sequence prediction model, determining a baseline reconstruction error distribution profile using the baseline behavioral sequences and the predicted baseline behavioral sequences, predicting test behavioral sequences from observed, test behavioral sequences using the sequence prediction model, determining a testing reconstruction error distribution profile using the observed test behavioral sequences and the predicted test behavioral sequences, and comparing the baseline reconstruction error distribution profile to the testing reconstruction error distribution profile to determine if an anomaly exists in a processor behavior of the processor based system.

Anomalous behavior detection in processor based systems

Techniques are disclosed for training a deep neural network (DNN) for reduced computational resource requirements. A computing system includes a memory for storing a set of weights of the DNN. The DNN includes a plurality of layers. For each layer of the plurality of layers, the set of weights includes weights of the layer and a set of bit precision values includes a bit precision value of the layer. The weights of the layer are represented in the memory using values having bit precisions equal to the bit precision value of the layer. The weights of the layer are associated with inputs to neurons of the layer. Additionally, the computing system includes processing circuitry for executing a machine learning system configured to train the DNN. Training the DNN comprises optimizing the set of weights and the set of bit precision values.

Dynamic adaptation of deep neural networks

Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

Systems and methods for optimizing operations of computing devices using deep neural networks

This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

Growing community of inventors

Princeton, NJ, United States of America

Aswin Nadamuni Raghavan