me

About Me

I am an Assistant Professor at the University of Houston, where I have been a faculty member since 2022. My research focuses on reliable and trustworthy computing at the edge, with an emphasis on building resilient and dependable computing systems. I am a recipient of the NSF CAREER Award for my research on Ad-Hoc Cloudless AI. I have also received the Teaching Excellence Award from the University of Houston in recognition of my contributions to teaching and student learning. Previously, I was a Postdoc (NSF CI-Fellow) at Duke University with Dr. Krishnendu Chakrabarty. I did my PhD from Washington State University where I was advised by Dr. Partha Pratim Pande and Dr. Janardhan Rao Doppa. You can find my CV here.
My current research focuses on reliable and trustworthy computing at the edge, spanning hardware, systems, and AI. I develop techniques to make resource-constrained and distributed computing systems more resilient to hardware failures, security threats, and unreliable operating environments, with a particular interest in enabling trustworthy AI beyond the cloud. A complete list of my work is available on my google scholar page. Following are some of my research areas (click to expand).


Relevant publications:
  1. B. K. Joardar, R. G. Kim, J. R. Doppa, P. P. Pande, D. Marculescu and R. Marculescu, “Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems," in IEEE Transactions on Computers, vol. 68, no. 6, pp. 852-866, 2019
  2. A. Deshwal, N. K. Jayakodi, B. K. Joardar, J. R. Doppa, and P. P. Pande, “MOOS: A Multi-Objective Design Space Exploration and Optimization Framework for NoC Enabled Manycore Systems,” in ACM Transactions on Embed. Comput. Syst. 18, 5s, Article 77, 2019
  3. A. I. Arka, B. K. Joardar, R. G. Kim, D. H. Kim, J. R. Doppa and P. P. Pande, "HeM3D: Heterogeneous Manycore Architecture Based on Monolithic 3D Vertical Integration," in ACM Transactions on Des. Autom. Electron. Syst, 26, 2, Article 16, 2021
  4. B. K. Joardar, A. Deshwal, J. R. Doppa, P. P. Pande and K. Chakrabarty, "High-Throughput Training of Deep CNNs on ReRAM-based Heterogeneous Architectures via Optimized Normalization Layers," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021


Relevant publications:
  1. B. K. Joardar, J. R. Doppa, P. P. Pande, H. Li and K. Chakrabarty, "AccuReD: High Accuracy Training of CNNs on ReRAM/GPU Heterogeneous 3D Architecture," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020
  2. A. I. Arka, B. K. Joardar, J. R. Doppa, P. P. Pande and K. Chakrabarty, “ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks,” in Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France, 2021 (Best Paper Nomination).

Relevant publications:
  1. B. K. Joardar, J. R. Doppa, P. P. Pande, H. Li and K. Chakrabarty, "AccuReD: High Accuracy Training of CNNs on ReRAM/GPU Heterogeneous 3D Architecture," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020
  2. B. K. Joardar, J. R. Doppa, H. Li, K. Chakrabarty and P. P. Pande, “Learning to Train CNNs on Faulty ReRAM-based Manycore Accelerators,” in International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES), 2021.
  3. X. Yang, S. Belakaria, B. K. Joardar, H. Yang, J. R. Doppa, P. P. Pande, K. Chakrabarty and H. Li, “Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise,” in International Conference on Computer Aided Design (ICCAD), 2021.

PhD openings

I'm looking for PhD students to join my group at University of Houston. The student should have a strong background knowledge of VLSI, computer architecture or machine learning. Significant coding experience in either C/C++/Python/Verilog is a must. Applicants should meet the UH admission requirements. If you are interested, please reach out to me at: bjoardar (at) central (dot) uh (dot) edu with your CV. Please mention your GPA, GRE and TOEFL scores (if available). Evidence of coding experience (e.g., Github repositories, Participation in online coding contests, etc.) will be preferred.