Abstract: Disclosed Methods, Apparatus, and articles of manufacture to dynamically enable and/or disable prefetchers are disclosed. An example apparatus include an interface to access telemetry data, the telemetry data corresponding to a counter of a core in a central processing unit, the counter corresponding to a first phase of a workload executed at the central processing unit; a prefetcher state selector to select a prefetcher state for a subsequent phase based on the telemetry data; and the interface to instruct the core in the central processing unit to operate in the subsequent phase according to the prefetcher state.

Abstract: A system for determining safety of a road segment may include at least one processor programmed to receive, from a first vehicle, first navigation information associated with the road segment. The first navigation information may include information collected by a first sensor of the first vehicle from an environment of the first vehicle. The at least one processor may also be programmed to receive, from a second vehicle, second navigation information associated with the road segment. The second navigation information may include information collected by a second sensor of the second vehicle from an environment of the second vehicle. The at least one processor may further be programmed to determine, based on the first navigation information and the second navigation information, a score representative of the safety of the road segment, and transmit, to a third vehicle, the score representative of the safety of the road segment.

Abstract: Described are mechanisms and methods for tracking user behavior profile over large time intervals and extracting observations for a user usage profile. The mechanisms and methods use machine learning (ML) algorithms embedded into a dynamic platform and thermal framework (DPTF) (e.g., Dynamic Tuning Technology) and predict device workloads using hardware (HW) counters. These mechanisms and methods may accordingly increase performance and user responsiveness by dynamically changing an Energy Performance Preference (EPP) based on a longer time workload analysis and workload prediction.

Abstract: In one embodiment, a method receives data regarding processing of a workload by a processor. The data is input into a prediction engine configured to classify the data into a plurality of workload classifications. Each workload classification describes different temporal behavior of the workload. Then, the method outputs a prediction for at least one of the plurality of workload classifications, wherein the prediction is used to control performance of the processor in an upcoming period of time.

Abstract: Systems and methods are disclosed for aggregating informational reports. In one implementation, at least one processor may be programmed to receive an informational vehicle report identifying a detected event; store the report in a database in association with a first cell; query a second cell within a predetermined distance of the first cell; and determine whether the second cell is associated with the detected event. When the second cell is associated with the detected event the processor may aggregate information from the first and second cells to provide an aggregated cluster and generate an event report based on the aggregated cluster. When the second cell is not associated with an information cluster associated with the detected event, the processor may generate the event report based on the stored informational vehicle report. The processor may then transmit the event report to one or more vehicles.

Abstract: Disclosed Methods, Apparatus, and articles of manufacture to dynamically enable and/or disable prefetchers are disclosed. An example apparatus include an interface to access telemetry data, the telemetry data corresponding to a counter of a core in a central processing unit, the counter corresponding to a first phase of a workload executed at the central processing unit; a prefetcher state selector to select a prefetcher state for a subsequent phase based on the telemetry data; and the interface to instruct the core in the central processing unit to operate in the subsequent phase according to the prefetcher state.

Abstract: In one embodiment, a method receives data regarding processing of a workload by a processor. The data is input into a prediction engine configured to classify the data into a plurality of workload classifications. Each workload classification describes different temporal behavior of the workload. Then, the method outputs a prediction for at least one of the plurality of workload classifications, wherein the prediction is used to control performance of the processor in an upcoming period of time.