Intel Corporation

Wake Forest University

Stephen J Tarsa

Gautham N Chinya

Hong Wang

Tsung-Han Lin

Perry H Wang

Helia A Naeimi

Julien Sebot

Gokce Keskin

Eric Kwesi Donkoh

Rangeen Basu Roy Chowdhury

Karthik Sankaranarayanan

Errin Wesley Fulp

Rustam Miftakhutdinov

Errin W Fulp

A system on a chip is described that comprises a processor and a set of memory components that store instructions, which when executed by the processor cause the system on a chip to: generate, by a set of data collectors of a telemetry subsystem, a set of streams of telemetry metadata describing operation of the processor, forward one or more streams of telemetry metadata from the set of streams of telemetry metadata to a set of machine learning-driven adaptation decision models, receive, from the set of machine learning-driven adaptation decision models, a set of configuration parameters for controlling operation of the processor based on the one or more streams of telemetry metadata, and modify operation of the processor based on the set of configuration parameters.

Reducing microprocessor power with minimal performance impact by dynamically adapting runtime operating configurations using machine learning

Disclosed embodiments relate to a prefetcher for delinquent irregular loads. In one example, a processor includes a cache memory, fetch and decode circuitry to fetch and decode instructions from a memory; and execution circuitry including a binary translator (BT) to respond to the decoded instructions by storing a plurality of decoded instructions in a BT cache, identifying a delinquent irregular load (DIRRL) among the plurality of decoded instructions, determining whether the DIRRL is prefetchable, and, if so, generating a custom prefetcher to cause the processor to prefetch a region of instructions leading up to the prefetchable DIRRL.

Prefetcher for delinquent irregular loads

Implementations of the disclosure provide a processing device comprising a branch predictor circuit to obtain a branch history for an application. The branch history comprising references to branching instructions associated with the application and an outcome of executing each branch. Using the branch history, a neutral network is trained to produce a weighted value for each branch of the branching instructions. Features of the branching instructions are identified based on the weighted values. Each feature identifying predictive information regarding the outcome of at least one branch of correlated branches having corresponding outcomes. A feature vector is determined based on the features. The feature vector comprises a plurality of data fields that identify an occurrence of a corresponding feature of the correlated branches with respect to the branch history. Using the feature vector, a data model is produced to determine a predicted outcome associated with the correlated branches.

Supporting learned branch predictors

Methods, systems, and computer program products for firewall policy optimization are disclosed. According to one method, a firewall policy including an ordered list of firewall rules is defined. For each rule, a probability indicating a likelihood of receiving a packet matching the rule is determined. The rules are sorted in order of non-increasing probability in a manner that preserves the firewall policy.

Methods, systems, and computer program products for network firewall policy optimization

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Cambridge, MA, United States of America

Stephen J Tarsa