Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Sharma, M.

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    Green intelligence for cloud data centers
    (Institute of Electrical and Electronics Engineers Inc., 2016) Karthik, C.; Sharma, M.; Maurya, K.; Chandrasekaran, K.
    In this paper the problem of energy consumption by large data centers has been tackled. Power consumption is major problem from both economic and environmental point of view. One of the main components of data centers is virtualization. We have addressed the problem of Virtual Machine (VM) consolidation in the data center servers using the technique of Bin Completion. Bin Completion is basically an artificial intelligence based algorithm used for bin packing problem. We have scaled up and modified the algorithm to fit our problem statement of VM consolidation and analysed the results obtained against Best Fit algorithm. After that we did an extensive study of the application of machine learning algorithms for the purpose of CPU utilisation prediction and analysed its effects on the overall energy consumption of a data center as well as the SLA violations. © 2016 IEEE.
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    Hardware Accelerator for Object Detection using Tiny YOLO-v3
    (Institute of Electrical and Electronics Engineers Inc., 2021) Sharma, M.; Rahul, R.; Madhusudan, S.; Deepu, S.P.; Sumam David, S.
    For applications that require object detection to be performed in real-time, this paper presents a custom hardware accelerator, implementing state of the art Tiny YOLO-v3 algorithm. The proposed architecture achieves a reasonable tradeoff between the speed of computation (measured in frames per second or FPS) and the hardware resources required. Each CNN layer is pipelined and parameterized to make the complete design re-configurable. The proposed hardware accelerator was synthesized using the SCL(Semi-Conductor Laboratory, India) 180 nm CMOS process and also using Vivado Xilinx software with Virtex Ultrascale+ FPGA as the target device. The pipelined architecture, along with other architectural novelties, provided a higher frame-rate of 32.1 FPS and a performance of 166.4 GOPS at 200 MHz clock frequency. © 2021 IEEE.
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    Deployment of Computer Vision Application on Edge Platform
    (Institute of Electrical and Electronics Engineers Inc., 2021) Geetha, V.; Kiran, C.; Sharma, M.; Rakshith Kumar, J.
    In our work, we propose a low cost device which will aid visually impaired people to understand what is in their surroundings without the requirement of internet. Current technology makes use of Cloud Architecture and would require internet to achieve this purpose. But these systems will not work in areas with poor internet connectivity. Edge platform built on Raspberry Pi powered with Intel Neural Compute Stick is used by us for this purpose. Multi Label Image Classification Deep Learning Model is trained in the cloud. It is later optimised and deployed on Edge Device which is Raspberry Pi. Setup also consists of PiCamera which will record the video and give it as input to deployed model. Model will describe the items present in video, basically describing the surroundings. The output is in the form of audio which is played through speakers, thus enabling visually impaired people to understand their surroundings without the requirement of internet. Deployment of popular Machine Learning and Deep Learning Models is also examined in the edge device and a comprehensive performance evaluation is performed. © 2021 IEEE.