Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Omer, Om J"'
With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks that constrain Von-Neuman architectures. However, the const
Externí odkaz:
http://arxiv.org/abs/2402.11780
Autor:
Thyagharajan, Anirud, Ummenhofer, Benjamin, Laddha, Prashant, Omer, Om J, Subramoney, Sreenivas
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art semantic segmentation models suffer from the part misclassification problem
Externí odkaz:
http://arxiv.org/abs/2111.08434
Autor:
Nori, Anant V., Bera, Rahul, Balachandran, Shankar, Rakshit, Joydeep, Omer, Om J., Abuhatzera, Avishaii, Kuttanna, Belliappa, Subramoney, Sreenivas
Deep Neural Network (DNN) inference is emerging as the fundamental bedrock for a multitude of utilities and services. CPUs continue to scale up their raw compute capabilities for DNN inference along with mature high performance libraries to extract o
Externí odkaz:
http://arxiv.org/abs/2011.11695
Autor:
Sreenivas Subramoney, Shankar Balachandran, Rahul Bera, Joydeep Rakshit, Anant V. Nori, Avishaii Abuhatzera, Belliappa Kuttanna, Omer Om J
Publikováno v:
ISCA
Deep Neural Networks (DNN) are used in a variety of applications and services. With the evolving nature of DNNs, the race to build optimal hardware (both in datacenter and edge) continues. General purpose multi-core CPUs offer unique attractive advan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b70cc6fafe6f74767e88a660abaa8a6
https://doi.org/10.1109/isca52012.2021.00022
https://doi.org/10.1109/isca52012.2021.00022
Autor:
Kalsi Gurpreet S, Akshay Krishna Ramanathan, Vijaykrishnan Narayanan, Pillai Kamlesh R, Tarun Makesh Chandran, Srivatsa Srinivasa, Sreenivas Subramoney, Omer Om J
Publikováno v:
MICRO
This paper presents a Look-Up Table (LUT) based Processing-In-Memory (PIM) technique with the potential for running Neural Network inference tasks. We implement a bitline computing free technique to avoid frequent bitline accesses to the cache sub-ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f88dc123098275209cf6759e2147a693
https://doi.org/10.1109/micro50266.2020.00020
https://doi.org/10.1109/micro50266.2020.00020
Publikováno v:
ICIP
Many emerging applications of Visual SLAM running on resource constrained hardware platforms impose very aggressive pose accuracy requirements and highly demanding latency constraints. To achieve the required pose accuracy under constrained compute b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66c6b4277dc23c121b23c4fdec44a676
https://doi.org/10.1109/icip40778.2020.9190889
https://doi.org/10.1109/icip40778.2020.9190889
Publikováno v:
ICRA
Sparse-indirect SLAM systems have been dominantly popular due to their computational efficiency and photometric invariance properties. Depth sensors are critical to SLAM frameworks for providing scale information to the 3D world, yet known to be plag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03a241be6e05da5f72d0621835d54c77
https://doi.org/10.1109/icra40945.2020.9196745
https://doi.org/10.1109/icra40945.2020.9196745
Autor:
Sreenivas Subramoney, Hong Wang, Belliappa Kuttanna, Eagle Jones, Jim Radford, Gopi Neela, Biji George, Srivatsava Jandhyala, Omer Om J, Dipan Kumar Mandal, Kalsi Gurpreet S, Santhosh Kumar Rethinagiri, Lance Hacking
Publikováno v:
DATE
Visual Inertial Odometry (VIO) is used for estimating pose and trajectory of a system and is a foundational requirement in many emerging applications like AR/VR, autonomous navigation in cars, drones and robots. In this paper, we analyze key compute
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b2e7c25b90e3e4cdb5645df8742f798
https://doi.org/10.23919/date.2019.8714921
https://doi.org/10.23919/date.2019.8714921