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:
One or more sensors gather data, one or more processors analyze the data, and one or more indicators notify a user if the data represent an event that requires a response. One or more of the sensors and/or the indicators is a wearable device for wireless communication. Optionally, other components may be vehicle-mounted or deployed on-site. The components form an ad-hoc network enabling users to keep track of each other in challenging environments where traditional communication may be impossible, unreliable, or inadvisable. The sensors, processors, and indicators may be linked and activated manually or they may be linked and activated automatically when they come within a threshold proximity or when a user does a triggering action, such as exiting a vehicle. The processors distinguish extremely urgent events requiring an immediate response from less-urgent events that can wait longer for response, routing and timing the responses accordingly.
Abstract:
Technologies for bio-chemically controlling operation of a machine include applying a bio-chemical agent to an operator of the machine and controlling an operational characteristic of the machine based on the presence of the bio-chemical agent on the operator. The operational characteristic of the machine may be controlled based on the presence or lack of the biochemical agent on the operator. In some embodiments, the bio-chemical agent may be configured to generate a bio-chemical trigger in response to exposure to a biochemical or biological characteristic of the operator. The operation of the machine may be controlled based on such bio-chemical trigger or reaction.
Abstract:
Various systems and methods for improving map and navigation data are described herein. An electronic navigation system for improving map and navigation data comprises a database access module to access a database of physiological information to obtain a biometric value, the biometric value associated with a location and a time; a processing module to determine whether the biometric value violates a threshold; and a display module to display a notification on a map when the threshold is violated, the map including an area around the location associated with the biometric value, and the notification displayed proximate to the location associated with the biometric value.
Abstract:
Techniques to determine depth for a number of wearable devices, which may be worn in layers are provided. A wearable device can receive measurements from sensors to include sensors from a number of other wearable devices. Based on the sensor measurements, the wearable device can determine a depth for the wearable devices. The depth can include an indication of which wearable device is closest to a user, which wearable device is closest to an external environment, or the like.
Abstract:
Technologies for determining a threat assessment based on fear responses comprises monitoring sensor data received from a sensor array located at a monitored site. The sensor data may include behavioral sensor data indicative of a physical behavior of individuals within the monitored site and physiological sensor data indicative of physiological characteristics of individuals within the monitored site. The threat assessment may be based on the behavioral sensor data and physiological sensor data. In some embodiments, context data related to the monitored site may be utilized analyze the behavioral sensor data and physiological sensor data and determine a threat assessment based thereon.
Abstract:
Technologies for determining a threat assessment based on fear responses comprises monitoring sensor data received from a sensor array located at a monitored site. The sensor data may include behavioral sensor data indicative of a physical behavior of individuals within the monitored site and physiological sensor data indicative of physiological characteristics of individuals within the monitored site. The threat assessment may be based on the behavioral sensor data and physiological sensor data. In some embodiments, context data related to the monitored site may be utilized analyze the behavioral sensor data and physiological sensor data and determine a threat assessment based thereon.
Abstract:
Techniques and architecture are disclosed for managing power use during operation of an electronic device capable of processing and/or playback of audio and/or video (AV) content. In some instances, the disclosed techniques/architecture can be used, for example: (1) to stop decoding and/or rendering of AV content upon detecting that a user wishes to stop or is otherwise unable to consume (e.g., hear/listen to or otherwise utilize) such AV content; and/or (2) to continue/re-enable decoding and/or rendering of AV content upon detecting that a user wishes or is otherwise able to continue/resume consumption thereof. In some cases, use of the disclosed techniques/architecture may reduce central processing unit (CPU) cycles, audio digital signal processing (DSP), rendering hardware usage, etc., and/or otherwise make more efficient use of battery charge, and thus may realize an improvement in battery life, for example, for a mobile/battery-operated device capable of AV processing and/or playback.
Abstract:
An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core including mixed-precision execution circuitry to execute one or more of the mixed-precision instructions to perform a mixed-precision dot-product operation comprising to perform a set of multiply and accumulate operations.
Abstract:
A mechanism is described for facilitating barriers and synchronization for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting thread groups relating to machine learning associated with one or more processing devices. The method may further include facilitating barrier synchronization of the thread groups across multiple dies such that each thread in a thread group is scheduled across a set of compute elements associated with the multiple dies, where each die represents a processing device of the one or more processing devices, the processing device including a graphics processor.