Intel Corporation

Juan Pablo Munoz

Daniel Cummings

Rajesh Poornachandran

Ravishankar Iyer

Eriko Nurvitadhi

Nilesh K Jain

Anthony Sarah

Ignacio J Alvarez Martinez

Chaunte W Lacewell

Tristan Webb

Sharath Nittur Sridhar

Anahita Bhiwandiwalla

Maciej Szankin

Souvik Kundu

The present disclosure is related to machine learning model swap (MLMS) framework for that selects and interchanges machine learning (ML) models in an energy and communication efficient way while adapting the ML models to real time changes in system constraints. The MLMS framework includes an ML model search strategy that can flexibly adapt ML models for a wide variety of compute system and/or environmental changes. Energy and communication efficiency is achieved by using a similarity-based ML model selection process, which selects a replacement ML model that has the most overlap in pre-trained parameters from a currently deployed ML model to minimize memory write operation overhead. Other embodiments may be described and/or claimed.

Machine learning model scaling system with energy efficient network data transfer for power aware hardware

The present disclosure is related to framework for automatically and efficiently finding machine learning (ML) architectures that are optimized to one or more specified performance metrics and/or hardware platforms. This framework provides ML architectures that are applicable to specified ML domains and are optimized for specified hardware platforms in significantly less time than could be done manually and in less time than existing ML model searching techniques. Furthermore, a user interface is provided that allows a user to search for different ML architectures based on modified search parameters, such as different hardware platform aspects and/or performance metrics. Other embodiments may be described and/or claimed.

Framework for optimization of machine learning architectures

Methods, apparatus, systems, and articles of manufacture are disclosed to optimize resources in edge networks. An example apparatus includes agent managing circuitry to invoke an exploration agent to identify platform resource devices, select a first one of the identified platform resource devices, and generate first optimization metrics for the workload corresponding to the first one of the identified platform resource devices, the first optimization metrics corresponding to a first path. The example agent is to further select a second one of the identified platform resource devices, generate second optimization metrics for the workload corresponding to the second one of the identified platform resource devices, the second optimization metrics corresponding to a second path. The example apparatus also includes benchmark managing circuitry to embed second semantic information to the workload, the second semantic information including optimized graph information and platform structure information corresponding to the second one of the identified platform resource devices, and reconfiguration managing circuitry to select the first path or the second path during runtime based on (a) service level agreement (SLA) information and (b) utilization information corresponding to the first and second identified platform resource devices.

Methods, systems, articles of manufacture and apparatus to optimize resources in edge networks

Disclosure herein are systems and methods for dynamically adjusting infrastructure items, such as street lights, construction signage, and/or other lighting elements. The systems and methods may include receiving environmental data for a sector containing the infrastructure items. A quality of infrastructure effectors located within the sector may be determined. A deviation from a standard infrastructure quality associated with the infrastructure effectors may be determined. A setting of the infrastructure items located in the sector may be changed to minimize the deviation from the standard infrastructure quality.

Dynamically adjusting an infrastructure item

Growing community of inventors

Folsom, CA, United States of America

Juan Pablo Munoz