| ²é¿´: 469 | »Ø¸´: 0 | |||
zgu888гæ (³õÈëÎÄ̳)
|
[½»Á÷]
ÈðµäÓÚĬ°Â´óѧÕÐÆ¸²©Ê¿ºó£¨ÊµÊ±Ç¶Èëʽϵͳ£¬ »úÆ÷ѧϰ·½Ïò£©
|
|
1) postdoc (2 years) in machine learning and embedded systems this project addresses real-time execution of concurrent multi-dnn workloads. deep neural networks (dnn) are widely deployed in resource-constrained embedded devices with limited processing and storage capabilities. there are many industry frameworks for supporting efficient dnn inference on resource-constrained embedded devices, but they do not address concurrent execution of multiple dnns. multi-dnn workloads bring additional challenges of resource contention due to sharing of common pus, and requires novel scheduling and coordination algorithms to ensure qos (quality-of-service) for each dnn in a multi-dnn workload. 2) postdoc (2 years) in real-time systems this project addresses the general area of real-time embedded systems. research topics include real-time scheduling theory, mixed-criticality systems, real-time multicore systems, real-time systems design, probabilistic timing analysis, safety and security issues, etc, with applications in safety-critical autonomous systems such as autonomous driving. the specific topic will be determined by considering both the applicant¡¯s research background and project requirements. please contact prof. gu at zonghua.gu @ umu.se with your resume. |
» ²ÂÄãϲ»¶
²ÄÁϵ÷¼Á
ÒѾÓÐ4È˻ظ´
266·Ö£¬Çó²ÄÁÏÏà¹Ø×¨Òµµ÷¼Á
ÒѾÓÐ13È˻ظ´
315Çóµ÷¼Á
ÒѾÓÐ6È˻ظ´
²ÄÁÏר˶ µ÷¼Á
ÒѾÓÐ10È˻ظ´
Ò»Ö¾Ô¸»ª±±µçÁ¦´óѧ£¨±±¾©£©£¬²ÄÁÏ¿ÆÑ§Ó빤³Ìѧ˶265£¬Çóµ÷¼Á
ÒѾÓÐ10È˻ظ´
Ò»Ö¾Ô¸0817»¯Ñ§¹¤³ÌÓë¼¼Êõ£¬Çóµ÷¼Á
ÒѾÓÐ13È˻ظ´
265Çóµ÷¼Á
ÒѾÓÐ17È˻ظ´
08¹¤¿Æ£¬295£¬½ÓÊÜ¿çרҵµ÷¼Á
ÒѾÓÐ8È˻ظ´
274Çóµ÷¼Á
ÒѾÓÐ8È˻ظ´
320·Ö£¬²ÄÁÏÓ뻯¹¤×¨Òµ£¬Çóµ÷¼Á
ÒѾÓÐ19È˻ظ´














»Ø¸´´ËÂ¥