Abstract: An apparatus to facilitate compute compression is disclosed. The apparatus includes a graphics processing unit including mapping logic to map a first block of integer pixel data to a compression block and compression logic to compress the compression block.

Abstract: In an example, an apparatus comprises a compute engine comprising a high precision component and a low precision component; and logic, at least partially including hardware logic, to receive instructions in the compute engine; select at least one of the high precision component or the low precision component to execute the instructions; and apply a gate to at least one of the high precision component or the low precision component to execute the instructions. Other embodiments are also disclosed and claimed.

Abstract: A mechanism is described for facilitating person tracking and data security in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting, by a camera associated with one or more trackers, a person within a physical vicinity, where detecting includes capturing one or more images the person. The method may further include tracking, by the one or more trackers, the person based on the one or more images of the person, where tracking includes collect tracking data relating to the person. The method may further include selecting a tracker of the one or more trackers as a preferred tracker based on the tracking data.

Abstract: In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.

Abstract: An integrated circuit (IC) package apparatus is disclosed. The IC package includes one or more processing units and a bridge, mounted below the one or more processing unit, including one or more arithmetic logic units (ALUs) to perform atomic operations.

Abstract: Described is a system for controlling epidural spinal cord stimulation. Using an Unscented Kalman Filter (UKF), the system receives sensed physiological signals from a subject and, based on the sensed physiological signals, estimating an unobservable state of a target area on the subject. A central pattern generator is then used to generate a stimulation pattern based on the unobservable state. The stimulation pattern is applied to the target area (e.g., spinal cord) of the subject using an electrode array. Receiving feedback, the UKF continuously updates a model of the spinal cord, which results in adjustment of the stimulation pattern as necessary.

Abstract: Described is a system for estimating ego-motion of a moving camera for detection of independent moving objects in a scene. For consecutive frames in a video captured by a moving camera, a first ego-translation estimate is determined between the consecutive frames from a first local minimum. From a second local minimum, a second ego-translation estimate is determined. If the first ego-translation estimate is equivalent to the second ego-translation estimate, the second ego-translation estimate is output as the optimal solution. Otherwise, a cost function is minimized to determine an optimal translation until the first ego-translation estimate is equivalent to the second ego-translation estimate, and an optimal solution is output. Ego-motion of the camera is estimated using the optimal solution, and independent moving objects are detected in the scene.

Abstract: The present disclosure provides for generating a spiking neural network. Generating a spiking neural network can include determining that a first input fan-in from a plurality of input neurons to each of a plurality of output neurons is greater than a threshold, generating a plurality of intermediate neurons based on a determination that the first input fan-in is greater than the threshold, and coupling the plurality of intermediate neurons to the plurality of input neurons and the plurality of output neurons, wherein each of the plurality of intermediate neurons has a second input fan-in that is less than the first input fan-in and each of the plurality of output neurons has a third input fan-in that is less than the first input fan-in.

Abstract: Embodiments described herein describe object recognition using a spiking neural network. Object recognition using a spiking neural network can include processing each of the plurality of base templates through a plurality of input neurons to generate a plurality of first spikes through the plurality of input neurons, providing the plurality of first spikes from the plurality of input neurons to each of a plurality of excitatory neurons (E-neurons), providing a plurality of second spikes from a plurality of inhibitory neurons (I-neurons) to the plurality of E-neurons to inhibit a spiking rate of the E-neurons, generating a plurality of weights at each of the plurality of E-neurons based on the plurality of first spikes and the plurality of second spikes, and classifying a pattern utilizing the plurality of input neurons, the plurality of E-neurons, and the plurality of weights at each of the E-neurons.

Abstract: Described is a system for compensating ego-translations in video captured with a moving camera. Translative ego-motion is estimated on a sequence of image frames captured by a moving camera by minimizing a cost function that is based on at least one image frame difference between consecutive image frames. An alternating one directional search is performed to minimize the cost function to find an optimal translation. The optimal translation is applied to the sequence of image frames, resulting in a sequence of image frames with ego-translations.

Abstract: Described is a system for compensating for ego-motion during video processing. The system generates an initial estimate of camera ego-motion of a moving camera for consecutive image frame pairs of a video of a scene using a projected correlation method, the camera configured to capture the video from a moving platform. An optimal estimation of camera ego-motion is generated using the initial estimate as an input to a valley search method or an alternate line search method. All independent moving objects are detected in the scene using the described hybrid method at superior performance compared to existing methods while saving computational cost.

Abstract: Systems and methods for supervised learning and cascaded training of a neural network are described. In an example, a supervised process is used for strengthening connections to classifier neurons, with a supervised learning process of receiving a first spike at a classifier neuron from a processing neuron in response to training data, and receiving an out-of-band communication of a second desired (artificial) spike at the classifier neuron that corresponds to the classification of the training data. As a result of spike timing dependent plasticity, connections to the classifier neuron are strengthened. In another example, a cascaded technique is disclosed to generate a plurality of trained neural networks that are separately initialized and trained based on different types or forms of training data, which may be used with cascaded or parallel operation of the plurality of trained neural networks.

Abstract: A spiking neural network (SNN) includes artificial neurons interconnected by artificial synapses to model a particular network. A first neuron emits spikes to neighboring neurons to cause a wave of spikes to propagate through the SNN. Weights of a portion of the synapses are increased responsive to the wave of spikes based on a spike timing dependent plasticity (STDP) rule. A second neuron emits spike to cause a chain of spikes to propagate to the first neuron on a path based on the increase in the synaptic weights. The path is determined to represent a shortest path in the particular network from a first network node represented by the second neuron to a second network node represented by the first neuron.

Abstract: A spike timing dependent plasticity (STDP) rule is applied in a spiking neural network (SNN) that includes artificial synapses bi-directionally connecting artificial neurons in the SNN to model locations within a physical environment. A first neuron is activated to cause a spike wave to propagate from the first neuron to other neurons in the SNN. Propagation of the spike wave causes synaptic weights of a subset of the synapses to be increased based on the STDP rule. A second neuron is activated after propagation of the spike wave to cause a spike chain to propagate along a path from the second neuron to the first neuron, based on the changes to the synaptic weights. A physical path is determined from the second to the first neuron based on the spike chain, and a signal may be sent to a controller of an autonomous device to cause the autonomous to navigate the physical path.

Abstract: A spiking neural network (SNN) is defined that includes artificial neurons interconnected by artificial synapses, the SNN defined to correspond to one or more numerical matrices in an equation such that weight values of the synapses correspond to values in the numerical matrices. An input vector is provided to the SNN to correspond to a numerical vector in the equation. A steady state spiking rate is determined for at least a portion of the neurons in the SNN and an approximate result of a matrix inverse problem corresponding to the equation is determined based on values of the steady state spiking rates.

Abstract: Various systems and methods for implementing unsupervised or reinforcement learning operations for a neuron weight used in a neural network are described. In an example, the learning operations include processing a spike train input at a neuron of a spiking neural network, applying a synaptic weight, and observing spike events occurring before and after the neuron processing based on respective spike traces. A synaptic weight update process operates to generate a new value of the synaptic weight based upon the spike traces, configuration values, and a reference weight value. A reference weight update process also operates to generate a new value of the reference value for significant changes to the synaptic weight. Reinforcement may be provided in some examples to implement changes to the reference weight in reduced time. In some examples, the techniques may be implemented in a neuromorphic hardware implementation of the spiking neural network.

Abstract: System and techniques for temporally encoded static spatial images are described herein. A static spatial image may be obtained. Here, the static spatial image defines pixel values over an area. A scan path may be selected. Here, the scan path defines a path across the area of the static spatial image. A windows is scanned (e.g., moved or slid) along the scan path on the static spatial image to produce changes in a portion of the window over time. The changes in the portion of the window are recorded along with respective times of the changes.

Abstract: A method to perform convolutions between arbitrary vectors includes estimating a first degree of match for a difference between a first vector having a plurality of first elements and a second vector having a plurality of second elements using a first cluster of coupled oscillators, estimating a second degree of match for the first vector using a second cluster of coupled oscillators, estimating a third degree of match for the second vector using a third cluster of coupled oscillators, deriving a first squared L2 norm from the first degree of match, deriving a second squared L2 norm from the second degree of match, deriving a third squared L2 norm from the third degree of match, adding the second squared L2 norm and the third squared L2 norm, and subtracting the first squared L2 norm to form a sum, and dividing the sum by two.

Abstract: Model-based neural control uses a model of a portion of a brain and provides feedback control to the model that is based on a received output from the model. A neuromorphic model-based control system includes a neuromorphic model that includes a neuromorphic network to model the brain portion. A synaptic time-multiplexed (STM) neural model-based control system includes an STM neural network to the model the brain portion. The control systems further include a feedback controller to receive an output of the neuromorphic model or STM neural network and to provide a feedback control input to control a model state of the neuromorphic model or the STM neural network.

Abstract: A method to perform convolutions between arbitrary vectors includes estimating a first degree of match for a difference between a first vector having a plurality of first elements and a second vector having a plurality of second elements using a first cluster of coupled oscillators, estimating a second degree of match for the first vector using a second cluster of coupled oscillators, estimating a third degree of match for the second vector using a third cluster of coupled oscillators, deriving a first squared L2 norm from the first degree of match, deriving a second squared L2 norm from the second degree of match, deriving a third squared L2 norm from the third degree of match, adding the second squared L2 norm and the third squared L2 norm, and subtracting the first squared L2 norm to form a sum, and dividing the sum by two.