CogniServe: Heterogeneous Server Architecture for Large-Scale Recognition

Authors:
Ravi Iyer;Sadagopan Srinivasan;Omesh Tickoo;Zhen Fang;Rameshkumar Illikkal;Steven Zhang;Vineet Chadha;Paul Stillwell;Seung Eun Lee
Affiliations:
Intel Labs;Intel Labs;Intel Labs;Intel Labs;Intel Labs;Intel Labs;Intel Labs;Intel Labs;Seoul National University of Science and Technology
Venue:
IEEE Micro
Year:
2011

Citing 0
Cited 4

Cost-effectively offering private buffers in SoCs and CMPs

Proceedings of the international conference on Supercomputing
Accelerating neuromorphic vision algorithms for recognition

Proceedings of the 49th Annual Design Automation Conference
Hardware Acceleration for Neuromorphic Vision Algorithms

Journal of Signal Processing Systems
Investigation on runtime partitioning of elastic mobile applications for mobile cloud computing

The Journal of Supercomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

As smart mobile devices become pervasive, vendors are offering rich features supported by cloud-based servers to enhance the user experience. Such servers implement large-scale computing environments, where target data is compared to a massive preloaded database. CogniServe is a highly efficient recognition server for large-scale recognition that employs a heterogeneous architecture to provide low-power, high-throughput cores, along with application-specific accelerators.