Alibaba Group Holding Limited

Samsung Electronics Co., Ltd.

Lingjie Xu

Wei Wei

John Wakefield Brothers

Santosh Abraham

Minghai Qin

Boris Beylin

Yen-Kuang Chen

Wei Zhang

Qinggang Zhou

Maxim Lukyanov

Lingling Jin

Alex Grosul

Ping Chen

Xinyang Yu

The present disclosure relates to a method for allocating resources of an accelerator to two or more neural networks for execution. The two or more neural networks may include a first neural network and a second neural network. The method comprises analyzing workloads of the first neural network and the second neural network, wherein the first neural network and second neural network each includes multiple computational layers, evaluating computational resources of the accelerator for executing each computational layer of the first and second neural networks, and scheduling computational resources of the accelerator to execute one computational layer of the multiple computation layers of the first neural network and to execute one or more computational layers of the multiple computational layers of the second neural network.

Maximizing resource utilization of neural network computing system

A method for power management based on synthetic machine learning benchmarks, including generating a record of synthetic machine learning benchmarks for synthetic machine learning models that are obtained by changing machine learning network topology parameters, receiving hardware information from a client device executing a machine learning program or preparing to execute a machine learning program, selecting a synthetic machine learning benchmark based on the correlation of the hardware information with the synthetic machine learning models, and determining work schedules based on the selected synthetic machine learning benchmark.

Methods and devices for power management based on synthetic machine learning benchmarks

Selective performance of deterministic computations for neural networks is disclosed, including: obtaining a statistical model for a selection layer of the neural network, the statistical model indicating probabilities that corresponding values are selected by the selection layer, the statistical model being generated using historical data; selectively performing a subset of a plurality of deterministic computations on new input data to the neural network, the plurality of deterministic computations being associated with the deterministic computation layer, the selective performance of the deterministic computations being based at least in part on the statistical model and generating a computation result; and outputting the computation result to another layer in the neural network.

Selective performance of deterministic computations for neural networks

A video processing apparatus includes a programmable hardware encoder configured to execute an encoding process on a plurality of input video frames. The video processing apparatus further includes a controller coupled with the programmable hardware encoder. The controller is configured to execute a set of instructions to cause the video processing apparatus to: determine first information of the plurality of input video frames, and adjust the encoding process based on the first information.

AI-assisted programmable hardware video codec

A method for reducing execution of redundant threads in a processing environment. The method includes detecting threads that include redundant work among many different threads. Multiple threads from the detected threads are grouped into one or more thread clusters based on determining same thread computation results. Execution of all but a particular one thread in each of the one or more thread clusters is suppressed. The particular one thread in each of the one or more thread clusters is executed. Results determined from execution of the particular one thread in each of the one or more thread clusters are broadcasted to other threads in each of the one or more thread clusters.

Redundancy elimination in single instruction multiple data/thread (SIMD/T) execution processing

Growing community of inventors

San Mateo, CA, United States of America

Lingjie Xu