Dec 22, 2016
Design Space Exploration for Deep Neural Nets for Advanced Driver Assistance Systems
ABOUT THE PROJECT
At a glance
Moreover, ADAS applications bring new opportunities as well as challenges to the DNN-train/DNN-deploy cycle that are not typically addressed in academic research. First, autonomous vehicles with six or more cameras and other sensors have the potential to create an unprecedented amount of training data. Second, this training data must be gathered, wirelessly, from diverse autonomous vehicles. Third, despite the high volumes of data, the DNN training must be done very quickly. For example, training data associated with obstacle avoidance (i.e. accident prevention) should be integrated and broadcast to autonomous vehicles as quickly as possible. In both formal and informal discussions with major companies and agencies involved with autonomous vehicles, the team understands there are many diverse opinions regarding the need for continuous training updates, nevertheless, the team believes that by addressing the most challenging scenario of continuous training, the requirements of other less demanding training scenarios can also be met.
With this background, the team will:
1. Perform Design-Space Exploration for DNN architectures/models, and hyper-parameters that are best suited for real world ADAS applications;
2. Perform Design-Space Exploration for DNN architectures that have a reduced number of weights. Fewer weights will results in DNN architectures that are faster to train (B2 above) and minimize communication requirements from client to cloud (B3 above);
3. Investigate weight/data compression techniques that further reduce the data required to represent weights; and
4. Investigate client/cloud computational trade-offs.
|Kurt Keutzer||Khalid Ashraf|
|Deep Neural Nets|
BAIR/CPAR/BDD Internal Weekly Seminar
The Berkeley Artificial Intelligence Research Lab co-hosts a weekly internal seminar series with the CITRIS People and Robots Initiative and the Berkeley Deep Drive Consortium. The seminars are every Friday afternoon in room 250 Sutardja Dai Hall from 3:10-4:10 PM, and are open to BAIR/BDD faculty, students, and sponsors. Seminars will be webcast live and recorded talks will be available online following the seminar.