Ganapati Bhat

I am a Ph.D. candidate at Arizona State University. At ASU I work with Prof. Umit Y. Ogras. Before joining ASU, I was a senior software engineer at Samsung Research and Development Institute, Bangalore, India. I obtained my B.Tech. in Electronics Enginnering from Indian Institute of Technology (ISM), Dhanbad, India in 2012.

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I will be joining the School of EECS at Washington State University as an Assistant Professor from Fall 2020. I am looking for PhD students to join me from Fall 2020. If you are interested in wearable IoT devices, heterogeneous mobile devices, flexible hybrid electronics, dynamic resource management, and health monitoring, please send me an email with your CV.

Research Interests

I am primarily interested in the design, optimization and application wearable IoT devices, heterogeneous mobile devices, flexible hybrid electronics, and health monitoring. Some of the topics in these areas that I am working on include energy management, energy harvesting, human activity recognition, dynamic thermal and power management, and resource management. I am also interested in applications of machine learning, dynamic programming, and convex optimization in real-world problems.

News
  • April 2020: Accepted a faculty position in the School of Electrical Engineering and Computer Science at Washington State University
  • March 2020: Paper on dynamic resource management accepted for publication in ACM Transactions on Design Automation of Electronic Systems!
  • January 2020: Paper on thermal analysis of multiprocessors accepted for publication in IEEE Transactions on Control Systems Technology!
  • January 2020: Invited talk at Indian Institute of Science, Bengaluru, India
  • December 2019: Paper accepted in VTS Special issue of IEEE Design and Test!
  • November 2019: Attending the ACM Student Research Competition at ICCAD 2019
  • October 2019: Best paper award at ESWEEK 2019!
Achievements and Recognitions
Research

Wearable IoT Devices

Wearable IoT devices have the transform multiple facets of our life by enabling applications such as unibuitious sensing, smart healthcare, and robotics. In my research, I focus on design and optimization of wearable IoT devices for health monitoring applications. In particular, my current research projects in this area include:

Flexible Hybrid Electronics Devices

The emerging Flexible Hybrid Electronics (FHE) devices combine the form-factor benefits of pure flexible substrates and performance benefits of rigid CMOS ICs. Using the FHE technology we desinged the experimental protytpe shown on the right. It combines a TI MCU, Invensense Motion Processing Unit, energy harvesting using photovoltaic cells, and communication using BLE. The device can be easily worn as a patch on the body, thus enabling continuous monitoring of the user motion. We have used this prototype in OpenHealth, our open-source hardware/software platform for health monitoring. We also implemented applications such as gesture recogniton and human activity recognition. In the future, we plan to use the derivatives of this board to conduct experiments with movement disorder patients.
Relevant papers: D&T'19, VTS'19, TECHConnect'19, ICCAD'16
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Energy Management for Wearable IoT Devices

Limited battery capacity is one of the major challenges for the widespread adoption of wearable IoT devices. Larger batteries make the device heavy and bulky, while smaller batteries provide limited lifetime. Therefore, we use solar energy harvesting to achieve energy-neutral operation for wearable device. Our approach, shown on the left side, first models the energy that can be harvested over a given period. Then, we use a dynamic programming approach to optimally allocate the harvested energy over the day. This ensures that the total energy consumed is equal to the harvested energy, thus eliminating battery charging requirements. Our algoritm also accounts for variations in the harvested energy and usage at runtime so that unforseen circumstances can be accounted for. We continue to work in this area by adding energy harvesting modalities and developing new approaches for energy management.

Relevant Papers: DAC'19, ICCAD'18

Human Activity Recognition

Human activity recognition aims to identify daily human activities, such as walking, sitting, and jogging. In this research, we used a wearable setup consiting of a stretch sensor and accelerometer to identify seven activities and transitions between them. A demonstration of our application is shown in the video on the right. We also developed an online learning approach to fine-tune the weights of our classifier for new users. The datasets of this work are available online on my GitHub page. We plan to extend this approach to monitor the symptoms of movement disorder patients.

Relevant Papers: TECS'19, DAC'19, ICCAD'18

Dynamic Thermal, Power, and Resource Management in Mobile Devices

Mobile devices are being used in multiple aspects of our lives including communication, healthcare, entertainment, and education. Modern mobile systems integrate mutiple CPU types, GPUs, and accelerators to achieve competitive performance and power consumption. The usage multiple resources introduces challenges in the thermal, power, and resource management on these devices. My research projects in mobile devices try to address these issues.
Dynamic Thermal and Power Management

The power consumption and temperature form a positive feedback loop, which causes a continuous increase in both until a steady state is reached. More specifically, the dynamic power consumption of the mobile device increase the temperature that increases the leakage power consumption. In a stable system, this feedback loop converges to a stable steady state temperature, while a thermal runaway occurs in an unstable system. A thermal runaway causes permanent damage to the device, therefore we need to analyze the stability of the system at runtime. My work addresses this question by developing closed-form conditions for the stability of the system.

Modern mobile devices increase the operating frequency of resources to meet the performance needs of the user. The increase in frequency causes an increase in the skin and device temperatures. Therefore, the devices need runtime algorithms to maintain skin and device temperatures under safe limits. Naively reducing the frequency of the resources is not sufficient as it leads to performance degradation. In my research, we developed predictive algorithms to maintain the temperature of the device within safe limits with minimal impact on the performance. We continuously predict the temperature in the future and take action whenever we predict that the temperature will rise beyond a given threshold. Moreover, we use optimization techniques to ensure that our actions cause minimal performance impact. Our algorithm reduces the number of thermal violations by one order. We continue to work in this area by developing algorithms that aim to reduce the frequency for the applications that cause thermal violations.

Relevant papers: DATE'19, TVLSI'18, TECS'17

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Illustration of the prediction of thermal steady state using simulations (black lines), experimental measurements (red line), and analytical prediction (red triangles)




Illustration of the power modeling methodology used in the thermal management work.

Dynamic Resource Management using Imitation Learning

Increase in the number of resources in mobile devices leads to a large number of possible configurations (number and frequency of active cores) for running applications. Each application has unique resource requirements for its optimal execution. Furthermore, the optimal configuration changes at runtime as a function of the change in the application characteristics. The default algorithms in mobile system use utilization to make resource management decisions. However, utilization alone does not provide insight into the application characteristics. Therefore, in our research we use performance counters to make runtime resource management decisions. Using these counters, we design imitation learning policies to choose the resource configurations at runtime. We plan to extend these methods to work on embedded high performance computing nodes.
Relevant papers: TVLSI'19, TECS'17
Publications

Book Chapter

Designing Wearable Systems-on-Polymer using Flexible Hybrid Electronics
Umit Y. Ogras, Ujjwal Gupta, Jaehyun Park, Ganapati Bhat
Printed Electronics: Technologies, Applications and Challenges. Nova Science Publishers, Inc.

Journal Papers

An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms
Sumit K. Mandal, Ganapati Bhat, Janardhan Rao Doppa, Partha Pratim Pande, Umit Y. Ogras
ACM Transactions on Design Automation of Electronic Systems, 2020 (Accepted, in press)

Analysis and Control of Power-Temperature Dynamics in Heterogeneous Multiprocessors
Ganapati Bhat, Suat Gumussoy, Umit Y. Ogras
IEEE Transactions on Control Systems Technology, 2020 (Accepted, in press)
arXiv / pdf

Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices
Jaehyun Park, Ganapati Bhat, Anish NK, Cemil S. Geyik, Umit Y. Ogras, Hyung Gyu Lee
MDPI Sensors, 2020

Determining Mechanical Stress Testing Parameters for FHE Designs with Low Computational Overhead
Ganapati Bhat, Hang Gao, Sumit K. Mandal, Umit Y. Ogras, Sule Ozev
IEEE Design & Test, 2020
pdf

An Ultra-Low Energy Human Activity Recognition Accelerator for Wearable Health Applications
Ganapati Bhat, Yigit Tuncel, Sizhe An, Hyung Gyu Lee, Umit Y. Ogras
ACM Transactions on Embedded Computing Systems (TECS) - ESWEEK Special Issue, 2019   [Best Paper Award]
pdf / slides / data

Dynamic Resource Management of Heterogeneous Mobile Platforms via Imitation Learning
Sumit K. Mandal, Ganapati Bhat, Chetan A. Patil, Janardhan Rao Doppa, Partha Pratim Pande, Umit Y. Ogras
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2019

OpenHealth: Open-Source Platform for Wearable Health Monitoring
Ganapati Bhat, Ranadeep Deb, Umit Y. Ogras
IEEE Design & Test, 2019
arXiv / project / data

Detection Mechanisms for Unauthorized Wireless Transmissions
Doohwang Chang, Ganapati Bhat, Umit Y. Ogras, Bertan Bakkaloglu, Sule Ozev
ACM Transactions on Design Automation of Electronic Systems (TODAES), 2018

Algorithmic Optimization of Thermal and Power Management for Heterogeneous Mobile Platforms
Ganapati Bhat, Gaurav Singla, Ali K. Unver, Umit Y. Ogras
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2018

Power-Temperature Stability and Safety Analysis for Multiprocessor Systems
Ganapati Bhat, Suat Gumussoy, Umit Y. Ogras
ACM Transactions on Embedded Computing Systems (TECS) - ESWEEK Special Issue, 2017
arXiv

DyPO: Dynamic Pareto-Optimal Configuration Selection for Heterogeneous MpSoCs
Ujjwal Gupta, Chetan A. Patil, Ganapati Bhat, Prabhat Mishra, Umit Y. Ogras
ACM Transactions on Embedded Computing Systems (TECS) - ESWEEK Special Issue, 2017

Conference Papers

REAP: Runtime Energy-Accuracy Optimization for Energy Harvesting IoT Devices
Ganapati Bhat, Kunal Bagewadi, Hyung Gyu Lee, Umit Y. Ogras
Design Automation Conference (DAC), 2019
arXiv / slides / data

Wearable IoT Devices for Health Monitoring
Ganapati Bhat, Yigit Tuncel, Sizhe An, Umit Y. Ogras
TechConnect Briefs, 2019
pdf

Sensor-Classifier Co-Optimization for Wearable Human Activity Recognition Applications
Anish NK, Ganapati Bhat, Jaehyun Park, Hyung Gyu Lee, Umit Y. Ogras
IEEE International Conference on Embedded Software and Systems (ICESS), 2019

Optimized Stress Testing for Flexible Hybrid Electronics Designs
Hang Gao, Ganapati Bhat, Umit Y. Ogras, Sule Ozev
IEEE VLSI Test Symposium (VTS), 2019   [Best Paper Candidate]

Power and Thermal Analysis of Commercial Mobile Platforms: Experiments and Case Studies
Ganapati Bhat, Suat Gumussoy, Umit Y. Ogras
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019
arXiv

Online Learning for Adaptive Optimization of Heterogeneous SoCs
Ganapati Bhat, Sumit K. Mandal, Ujjwal Gupta, Umit Y. Ogras
International Conference on Computer-Aided Design (ICCAD), 2018

Online Human Activity Recognition using Low-Power Wearable Devices
Ganapati Bhat, Ranadeep Deb, Vatika Vardhan Chaurasia, Holly Shill, Umit Y. Ogras
International Conference on Computer-Aided Design (ICCAD), 2018
arXiv / slides / data

Energy-Optimal Gesture Recognition using Self-Powered Wearable Devices
Jaehyun Park, Ganapati Bhat, Cemil S. Geyik, Hyung Gyu Lee, Umit Y. Ogras
IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018

Near-Optimal Energy Allocation for Self-Powered Wearable Systems
Ganapati Bhat, Jaehyun Park, Umit Y. Ogras
International Conference on Computer-Aided Design (ICCAD), 2017

Fluid Wireless Protocols: Energy-Efficient Design and Implementation
Ganapati Bhat, Sharanya Srinivas, Vamsi Chagari, Jaehyun Park, Thomas McGiffen, Hyunseok Lee, Daniel W. Bliss, Chaitali Chakrabarti, Umit Y. Ogras
IEEE/ACM Symposium on Embedded Systems for Real-Time Multimedia, 2017

Multi-Objective Design Optimization for Flexible Hybrid Electronics
Ganapati Bhat, Ujjwal Gupta, Nicholas Tran, Jaehyun Park, Sule Ozev, Umit Y. Ogras
International Conference on Computer-Aided Design (ICCAD), 2016

Newsletters

What is Flexible Hybrid Electronics?
Ganapati Bhat, Umit Y. Ogras
ACM Special Interest Group on Design Automation Newsletter, Vol. 49, No. 11, November 01, 2019
pdf

Use of Wearable Devices in Health Monitoring: A Review of Recent Studies
Ganapati Bhat, Ranadeep Deb, Umit Y. Ogras
IEEE Technical Committee on Cyber-Physical Systems Newsletter, Volume 4, Issue 2, August 01, 2019
pdf


Adapted from Jon Barron's webpage.