公开(公告)号：US20200034710A1

公开(公告)日：2020-01-30

申请号：US16522411

申请日：2019-07-25

Applicant: DeepScale, Inc.

Inventor： Harsimran Singh Sidhu ,  Paras Jagdish Jain ,  Daniel Paden Tomasello ,  Forrest Nelson Iandola

IPC: G06N3/08 ,  G06N3/10 ,  G05B13/02 ,  G06F9/455 ,  G05D1/00 ,  G05D1/02

Abstract: A model training and implementation pipeline trains models for individual embedded systems. The pipeline iterates through multiple models and estimates the performance of the models. During a model generation stage, the pipeline translates the description of the model together with the model parameters into an intermediate representation in a language that is compatible with a virtual machine. The intermediate representation is agnostic or independent to the configuration of the target platform. During a model performance estimation stage, the pipeline evaluates the performance of the models without training the models. Based on the analysis of the performance of the untrained models, a subset of models is selected. The selected models are then trained and the performance of the trained models are analyzed. Based on the analysis of the performance of the trained models, a single model is selected for deployment to the target platform.

公开(公告)号：US20180188733A1

公开(公告)日：2018-07-05

申请号：US15855749

申请日：2017-12-27

Applicant: DeepScale, Inc.

Inventor： Forrest Nelson Iandola ,  Donald Benton MacMillen ,  Anting Shen ,  Harsimran Singh Sidhu ,  Daniel Paden Tomasello ,  Rohan Nandkumar Phadte ,  Paras Jagdish Jain

IPC: G05D1/02 ,  G06F17/50

CPC classification number: G05D1/024 ,  G05D1/0246 ,  G05D1/0255 ,  G05D1/0257 ,  G06F17/5009 ,  G06F17/5095 ,  G06N3/0454 ,  G06N3/084 ,  G06N20/00

Abstract: An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.

公开(公告)号：US20180275658A1

公开(公告)日：2018-09-27

申请号：US15934899

申请日：2018-03-23

Applicant: DeepScale, Inc.

Inventor： Forrest Nelson Iandola ,  Donald Benton MacMillen ,  Anting Shen ,  Harsimran Singh Sidhu ,  Paras Jagdish Jain

IPC: G05D1/00 ,  G06K9/62 ,  B60R11/04 ,  G06F17/50

Abstract: An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.