Tusimple, Inc.

Beijing Tusen Zhitu Technology Co., Ltd.

Dinghua Li

Lei Su

Jiangming Jin

Yixin Yang

Wei Liu

Zehua Huang

Siyuan Liu

Yifan Gong

Yuwei Hu

The embodiments of this application provide a method and device for optimizing neural network. The method includes: binarizing and bit-packing input data of a convolution layer along a channel direction, and obtaining compressed input data; binarizing and bit-packing respectively each convolution kernel of the convolution layer along the channel direction, and obtaining each corresponding compressed convolution kernel; dividing the compressed input data sequentially in a convolutional computation order into blocks of the compressed input data with the same size of each compressed convolution kernel, wherein the data input to one time convolutional computation form a data block; and, taking a convolutional computation on each block of the compressed input data and each compressed convolution kernel sequentially, obtaining each convolutional result data, and obtaining multiple output data of the convolution layer according to each convolutional result data.

Method and device for optimizing neural network

Techniques for analysis of autonomous vehicle operations are described. As an example, a method of autonomous vehicle operation includes storing sensor data from one or more sensors located on the autonomous vehicle into a storage medium, performing, based on at least some of the sensor data, a simulated execution of one or more programs associated with the operations of the autonomous vehicle, generating, based on the simulated execution of the one or more programs and as part of a simulation, one or more control signal values that control a simulated driving behavior of the autonomous vehicle, and providing a visual feedback of the simulated driving behavior of the autonomous vehicle on a simulated road.

Autonomous vehicle simulation system

The present disclosure provides a method, an apparatus and a system for multi-module scheduling, capable of solving the problem associated with inconsistency in data inputted to a computing module in the multi-module scheduling technique in the related art. The method includes: reading, by a master process, a pre-stored configuration file storing a directed computation graph; initializing, by the master process, states of all the nodes and connecting edges in the directed computation graph initially in computation in a current computing period; determining a node to be called based on the computation direction in the directed computation graph and the states of the nodes, the node to be called comprising a node having all of its input edges in a complete state; transmitting, to the computing module in the slave process corresponding to the node to be called, a call request of Remote Process Call (RPC) to execute the computing module; updating the state of the node and the state of each output edge of the node upon receiving a response to the call request; and proceeding with a next computing period upon determining that the states of all the nodes have been updated.

Method, apparatus, and system for multi-module scheduling

The present disclosure provides a method, an apparatus and a system for multi-module scheduling, capable of solving at least one of the problems associated with the multi-module scheduling technique in the related art, i.e., inconsistency in data inputted to a computing module, and a significant delay or low throughput in data transmission between computing modules. The method includes: reading, by a master process, a pre-stored configuration file storing a directed computation graph; initializing, by the master process, states of the nodes and connecting edges in a current computing period; determining a node to be called based on the computation direction of the directed computation graph and the states of the nodes, the node to be called comprising a node having all of its input edges in a complete state; transmitting, to the computing module in the slave process corresponding to the node to be called, a call request of Remote Process Call (RPC) to execute the computing module; updating the state of the node and the state of each output edge of the node upon receiving a response to the call request; and proceeding with a next computing period after determining that the states of all the nodes in the directed computation graph have been updated.

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Beijing, China

Dinghua Li