Baidu USA LLC

Baidu Online Network Technology (beijing) Co., Ltd.

Baidu.com Times Technology (beijing) Co. Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Apollo Autonomous Driving USA LLC

Haofeng Kou

Yingze Bao

Kuipeng Wang

Qiang Zhou

Yanwen Fan

Renyi Zhou

Yuexiang Hu

Yanghua Fang

Peng Fu

Lei Wang

Yueqiang Cheng

Shu Jiang

Manjiang Zhang

Zhen Chen

Le Kang

Hao Liu

Davy Huang

Zhiyu Cheng

Xing Li

Xiaoyi Zhu

Shengyi He

Szu-Hao Wu

Xuejun Wang

Helen K Pan

Huimeng Zheng

Ruichang Cheng

Fuyang Zhao

A system obtains a performance profile corresponding to times taken to perform an inferencing by a machine learning (ML) model using a different number of processing resources from a plurality of processing resources. The system determines one or more groupings of processing resources from the plurality of processing resources, each grouping includes one or more partitions. The system calculates performance speeds corresponding to each grouping based on the performance profile. The system determines a grouping having a best performance speed from the calculated performance speeds. The system partitions the processing resources based on the determined grouping to perform the inferencing.

System on chip parallel computing of ML services and applications for partitioning resources based on the inference times

In one embodiment, an image sensor captures an image and passes the image to an image processing system that detects one or more vehicle objects and one or more human objects, using a single detection function having a single artificial intelligence (AI) model. Each of the detected objects correspond to a portion of the captured image. For each vehicle object, the corresponding image portion is processed to determine a plurality of properties of the vehicle, including, e.g., type (truck, van, sedan, motorcycle, etc.), color, and license number. For each human object, the corresponding portion of the image is processed to determine a plurality of properties of the human, e.g., gender, approximate age, skin tone, eye color, hair color, and facial landmarks for facial recognition. The captured image is annotated to describe the objects and their properties, and annotated image is transmitted to a service for use by an application.

Method for deep neural network functional module deduplication

In one embodiment, a microcontroller unit (MCU) receives an expected state of an autonomous driving vehicle (ADV) from a controller of the ADV, where the controller controls motions of the ADV using a control algorithm. The MCU receives sensor data from one or more sensors of the ADV. The MCU determine an actual state of the ADV based on the sensor data. The MCU determines a performance metric of the control algorithm based on the expected state and the actual state. In response to determining the performance metric has satisfied a predetermined condition, the MCU determines a plurality of weight values for the control algorithm. The MCU sends the plurality of weight values to the control system to tune one or more weight parameters of the control algorithm using the plurality of weight values, where the controller controls the ADV using the tuned control algorithm.

On-board parameter tuning for control module for autonomous vehicles

Described herein are systems and methods for object detection to achieve hard real-time performance with low latency. Real-time object detection frameworks are disclosed. In one or more embodiments, a framework comprises a first CPU core, a second CPU core, and a plurality of shaves. In one or more embodiments, the first CPU core handles general CPU tasks, while the second CPU core handles the image frames from a camera sensor and computation task scheduling. In one or more embodiments, the scheduled computation tasks are implemented by the plurality of shaves using at least one object-detection model to detect an object in an image frame. In one or more embodiments, computation results from the object-detection model with a higher detection probability is used to form an output for object detection. In one or more embodiments, the object-detection models share some parameters for smaller size and higher implementing speed.

Systems and methods for low-power, real-time object detection

Embodiments disclose an artificial intelligence chip and a convolutional neural network applied to the artificial intelligence chip comprising a processor, at least one parallel computing unit, and a pooling computation unit. The method comprises: dividing a convolution task into convolution subtasks and corresponding pooling subtasks; executing convolution subtasks at different parallel computing units, and performing convolution, batch normalization, and non-linear computing operation in a same parallel computing unit; sending an execution result of each parallel computing unit from executing the convolution subtask to the pooling computation unit for executing the corresponding pooling subtask; merging executing results of the pooling computation unit from performing pooling operations on the executing results outputted by respective convolution subtasks to obtain an execution result of the convolution task. This can reduce data transport, such that operations of the convolutional neural network may be accomplished with lower power consumption and less time in an edge device.

Hardware-software co-design for accelerating deep learning inference

An information processing method, a device, a system, and a storage medium. The information processing method includes: an AI camera first obtains real-time data in a unmanned retail scenario and performs a front-end processing on the real-time data based on a neural network model, where the front-end processing includes any one or more of commodity identifying and human body monitoring, and then transmits a result of the front-end processing to a server, where the result of the front-end processing is used to trigger the server to perform face recognition and/or determine a flow direction of a commodity according to the result of the front-end processing. The cost of the entire unmanned retail distributed system and the pressure on data transmission bandwidth can be reduced, and system scalability as well as the performance of the solution to the unmanned retail can be improved effectively.

Information processing method, device, system and storage medium

The present application provides a method, an apparatus, a device and a system for processing commodity identification and a storage medium, where the method includes: receiving image information transmitted by a camera apparatus and a distance signal transmitted by a distance sensor corresponding to the camera apparatus; determining a start frame and an end frame for a pickup behavior of a user according to the image information and the distance signal; and determining, according to the start frame and the end frame for the pickup behavior of the user, information of a commodity taken by the user. By performing a commodity identification on the start frame and the end frame for the pickup behavior of the user, and determining the information of the commodity taken by the user, commodity identification efficiency is effectively improved.

Method, apparatus, device and system for processing commodity identification and storage medium

Growing community of inventors

Sunnyvale, CA, United States of America

Haofeng Kou