Vmware, Inc.

Beijing Dajia Internet Information Technology Co., Ltd.

Tiejun Chen

Shreekanta Das

Gavin Lu

Amit Vasant Patil

Ravishankar Chamarajnager

Niranjan Maka

Vasudev Yendapally

Zhao Gao

Mandar Nadgouda

Elaine Zhao

Mahesh Kumar

Kit Colbert

Amol Khare

Mazhar I Memon

Hao Yang

Hong Yue

Yuxin Kou

Yinghua Chen

Peter Buckingham

Chris Wolf

Yongtao Huang

Zitong Xu

Disclosed are various approaches for the partitioning of virtualization on systems with multiple core processors. In one approach, hardware extensions for virtualizations are enabled on one or more first cores of a plurality of cores of the processor. The hardware extensions for virtualization are disabled on one or more second cores of the plurality of cores. A virtual machine instance is executed on the first cores having the hardware extensions for virtualization enabled. A real-time operating system is executed on the second cores having the hardware extensions for virtualization disabled.

Hybrid partitioning of virtualization on multiple core systems

Disclosed is a system for converting a high-level runtime model to a low-level runtime model where the high-level runtime model runs on a client computer system, and the low-level runtime model runs on a server computer system. The server system has installed thereon a pool of hardware accelerators, and the low-level runtime model is targeted to the pool of accelerators. Outputs of the low-level runtime model are returned to the high-level runtime model as if the high-level runtime model computed the outputs.

Building a unified machine learning (ML)/ artificial intelligence (AI) acceleration framework across heterogeneous AI accelerators

Disclosed are various examples of providing AI accelerator access as a service at the edge. In some embodiments an artificial intelligence (AI) accelerator device identifier is transmitted to register an AI accelerator with the AI broker service. An AI processing request for the AI accelerator is received from a networked computing device. A bus redirect of the AI accelerator to the networked device is enabled. An AI workload is performed controlled by the networked device through the bus redirect.

Edge AI accelerator service

Disclosed herein is the integration into edge nodes of a telecommunications network system of client computer system and server computer system where the server computer system includes a pool of shareable accelerators and the client computer runs an application program that is assisted by the pool of accelerators. The edge nodes connect to user equipment, and some of the user equipment can themselves act as one of the client computer systems. In some embodiments, the accelerators are GPUs, and in other embodiments, the accelerators are artificial intelligence accelerators.

Edge artificial intelligence (AI) computing in a telecommunications network

Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a baseline behavior profile for a gateway virtual machine is transmitted from a management service to a gateway security process executed in a gateway device. The management service receives an anomaly notification including an indication of an anomaly from the baseline behavior profile. The managements service generates a user interface that shows a description of the anomaly.

Threat detection and security for edge devices

Disclosed are various examples of providing edge component redirection for IoT analytics groups. In some embodiments, an Internet-of-Things (IoT) analytics group is identified. The IoT analytics group includes an IoT device that communicates through an interface device of a first edge device. A grouping interface policy is generated to specify a bus redirect from the first edge computing device to a second edge computing device. The grouping interface policy is transmitted for implementation using the edge devices.

Edge component redirect for IoT analytics groups

A method for generating a video can include: acquiring a profile of a video template, the profile being configured to describe one or more reference materials of the video template and an animated special effect corresponding to each of the one or more reference materials; acquiring the one or more reference materials based on the profile; acquiring a target material; acquiring at least one replaced reference material by replacing at least one of the one or more reference materials with the target material; and generating a target video with the animated special effect by rendering the one or more replaced reference material based on the profile.

Method for generating video, and electronic device and readable storage medium thereof

Disclosed are various examples for threat detection and security for edge devices in communication with Internet-of-Things (IoT) devices. In one example, a profile is associated with a virtual machine of a gateway device. The profile includes an expected behavior for the virtual machine. The virtual machine is executed by a hypervisor of the gateway device. An actual behavior for the virtual machine is determined. A remedial action is performed. The remedial action is based on an anomaly between the expected behavior and the actual behavior.

Disclosed are various examples of providing edge component redirection for IoT analytics groups. In some embodiments, a grouping interface policy is received for an Internet-of-Things (IoT) device that communicates through an interface device of a first edge computing device. The grouping interface policy indicates that the IoT device is assigned to a second edge computing device. A bus redirect of the interface device is enabled. The bus redirect routes traffic between the IoT device and the second edge computing device.

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Beijing, China

Tiejun Chen