Avago Technologies International Sales Pte. Limited

Broadcom Corporation

Avago Technologies General IP (singapore) Pte. Ltd.

Prashant Katre

Rajesh Mamidwar

Sanjeev Sood

David Chaohua Wu

Anand Tongle

Xuemin Chen

Yong Li

Amit Rajendra Agarwal

Darren Duane Neuman

Predrag Kostic

Flaviu Dorin Turean

Hosahalli Rajarao Srinivas

Fabian Alberto Russo

Brett Tischler

Binh Vo

Gary Jacob Skerl

Craig Arlen Detrick

Darren Tokushige

James Ching-Shau Yik

Bismaya Parida

Gregory J Goodemote

Arnab Bhattacharya

The disclosure describes systems and methods for dynamically enabling and disabling data aggregation control. A device can communicate with an access point or a station. A first wireless chip in the access point can establish a wireless connection with a second wireless chip in the station. The first wireless chip and second wireless chip can negotiate during setup of the wireless connection whether to enable aggregation of data via the wireless connection. The device can detect that a flow of packets traversing the wireless connection corresponds to a low-latency application. Upon detection, the device can communicate information to the first wireless chip and the second wireless chip to cause the first wireless chip and the second wireless chip to dynamically disable the aggregation of data being communicated by the low-latency application over the wireless connection.

Systems and methods for dynamically enabling and disabling aggregation control

A system for multimedia content recognition includes a cloud server and a media client including a silicon-on-chip (SoC) device to communicate with the cloud server via a network. The SoC device includes a local area network (LAN) interface to receive media content from a media source and a media monitor to analyze the received media content and to generate signature information for transmission to the cloud server or for a local analysis. The SoC device further includes an inference engine to locally analyze the signature information to detect an unauthorized access.

Multimedia content recognition with local and cloud-assisted machine learning

An apparatus and method for providing ML processing for one or more ML applications operating on one or more Internet of Things (IoT) devices includes receiving a ML request from an IoT device. The ML request can be generated by a ML application operating on the IoT device and include input data collected by the first ML application. A ML model to perform ML processing of the input data included in the ML request is identified and provided to an ML core for ML processing along with the input data included in the first ML request. The ML core produces ML processing output data based on ML processing by the ML core of input data included in the ML request using the ML model. The ML processing output data can be transmitted to the IoT device.

Machine-learning application proxy for IoT devices including large-scale data collection using dynamic servlets with access control

A network device providing audio-video packet delivery over a local area network may include a memory and at least one processor. The at least one processor may be configured to receive data packets from a server, the data packets being addressed to an electronic device downstream from the network device. The at least one processor may be further configured to determine whether the data packets satisfy an offload condition. The at least one processor may be further configured to, when the data packets satisfy the offload condition: transmit, to the server, acknowledgement packets corresponding to the received data packets, store the data packets in a buffer, transmit the data packets to the electronic device, and retransmit the data packets to the electronic device responsive to request therefor, the retransmitted data packets being retrieved from the buffer.

Audio-video (AV) packet delivery over a local area network

A device implementing the subject wireless communication system may include one or more memories, and one or more processors coupled to the one or more memories. In some aspects, the one or more processors are configured to cause receiving a first frame comprising an indication that the station has data buffered at an access point, determining, in response to the first frame, whether a measured buffer depth exceeds a predetermined threshold, receiving a second frame comprising a predetermined amount of buffered data based on the measured buffer depth, transitioning, after the predetermined amount of buffered data is received, into a sleep mode for a first predetermined duration when the measured buffer depth exceeds the predetermined threshold, and transitioning into the sleep mode for a second predetermined duration less than the first predetermined duration when the measured buffer depth does not exceed the predetermined threshold.

Power consumption optimization of wireless communication systems for content delivery

A device for decoupling audio-video (AV) traffic processing from non-AV traffic processing may include a first processor and a second processor. The first processor may be configured to establish a network connection with a client device, determine whether the network connection is associated with AV traffic, transfer the network connection to a second processor when the network connection is associated with AV traffic, and process non-AV traffic associated with the network connection when the network connection is not associated with AV traffic. The second processor may be configured to receive the network connection from the first processor and process the AV traffic associated with the network connection.

Decoupling audio-video (AV) traffic processing from non-AV traffic processing

A system for adaptive audio video (AV) stream processing may include at least one processor and a switch device. The switch device may be configured to route AV traffic to the processor, and to receive AV traffic from the processor and provide the AV traffic to a client device via one or more channels. The processor may monitor a transcoder buffer depth and depths of buffers associated with channels over which the AV traffic is being transmitted. The processor may adaptively modify one or more attributes associated with the AV traffic based at least on the monitored buffer depths. For example, the processor may adaptively adjust a bit rate associated with transcoding the AV traffic based at least on the transcoder buffer depth. The processor may utilize the depths of the buffers associated with the channels to adaptively adjust the amount of AV traffic provided for transmission over the channels.

Adaptive audio video (AV) stream processing

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San Diego, CA, United States of America

Prashant Katre