Shaswot Shresthamali
Contact
Information

Room 755
shaswot.shresthamali@cpc.ait.kyushu-u.ac.jp
Kyushu University
shaswot.com
West 2nd Building, 744 Motooka, Nishi-ku, Fukuoka 819-0395, JAPAN+81-092-802-3668

Education

Ph.D., Information Science and Technology,
The University of Tokyo, Japan

March 2021

Dissertation Advisor: Prof. Hiroshi Nakamura
Dissertation Title: Reinforcement Learning-Based Optimization in Energy Harvesting WirelessSensor Nodes
Master of Information Science and Technology,
The University of Tokyo, Japan

March 2018

Dissertation Advisors: Prof. Hiroshi Nakamura, Prof. Masaaki Kondo
Dissertation Title: Adaptive Power Management of Solar Energy Harvesting Sensor Node byReinforcement Learning
B.E., Electronics and Communication Engineering,
Tribhuvan University, Nepal

October 2012

Final Year Project Advisor: Prof. Dinesh Kumar Sharma
Final Year Project: Digital Audio Processor
Employment

Research Associate Professor,
CPC Laboratory,
Department of Advanced Information Technology,
Kyushu University,
Japan

April 2024–Present

Project Assistant Professor,
Kondo Laboratory,
Department of Information and Computer Science,
Keio University,
Japan

April 2023–March 2024

Researcher,
Kondo Laboratory,
Department of Information and Computer Science,
Keio University,
Japan

April 2021–March 2023

Research Fellow,
Japan Society for the Promotion of Science (JSPS),Tokyo, Japan

April 2018–March 2021

Engineer,Ridge-i, Tokyo, Japan
Assistant Professor,
Sagarmatha Engineering College,Tribhuvan University, NepalFebruary 2018–August 2018

November 2012–October 2014

Shaswot Shresthamali — Curriculum Vitae
ResearchInterests

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Reinforcement Learning (RL), Deep RL, Off-policy RL, Distributed RL, Quantum Computation,
Deep Learning Accelerators.
I am currently researching on novel techniques and architectures to accelerate Deep Neural Networks(DNN). I am also researching on integration of High Performance Computing (HPC) systems with
Quantum Computers (QC) as a member in the QC-HPC Group of the SQAI project.
My doctoral research deals with RL-based methods for energy scheduling in energy-harvesting
wireless sensor nodes. The focus is on applied RL and its relation to neural network function
approximation, off-policy learning and distributed learning.

Honors andAwards

Grant-in-Aid for Early-Career Scientists, JSPS

2024–2027

Young Researcher Award, Information Processing Society of Japan (IPSJ)
DC1 Fellowship, JSPS

2018–2021

Grants-in-Aid for Scientific Research, JSPS

2018–2021

Young Researcher Award, Information Processing Society of Japan (IPSJ)

TeachingExperience2016

Japanese Government MEXT Scholarship,

2015-2018

College Fellowship, Pulchowk Campus, Tribhuvan University

2008–2012

Assistant Professor, Sagarmatha Engineering College, Tribhuvan University
Digital Signal Processing
Advanced Electronics

Mentoring/Supervision

2022

Winter 2013
Winter 2012, 2013

Instrumentation II

Winter 2013

Embedded Systems

Winter 2013

Electronic Devices and Circuits

Winter 2013

Electric Circuit Theory

Summer 2013

Basic Electronics Engineering

Summer 2013

Graduate Students
Yikai Mao (Doctoral student)

April 2022–Present

Thet Htar Su (Doctoral student)

April 2023–PresentDavide Laureti (Master student)

November 2023–Present

Jules Feron (Master student)November 2023–Present

Maelle Gabens (Master student)

November 2023–Present

Sugimoto Hirotada (Bachelor student)

November 2022–Present

Lorenzo Sonnino (Master student)Interns
Chander Shekhar

November 2021–October 2023

July 2019

Shaswot Shresthamali — Curriculum Vitae

ProfessionalActivities,
Outreach, andService

3 of 5Invited Speaker
Annual Nepal AI School (NAAMI)“Learning to Learn Linear Algebra”

December 20, 2021

Nepal Engineers Association Japan (NEAJ)“Sharing Professional Experience and Academic Research Work”

November 27, 2021

Nepal-Japan Educational Dialogue
Webinar: “Higher Education Opportunities in Japan”November 26, 2021

The University of Tokyo Alumni Association Nepal (UTAAN)
Annual Benkyokai

April 17, 2021

Program Committee
3rd Workshop on Machine Learning on Edge inSensor Systems (SenSys-ML-2024)

May 2024

2024 IEEE 17th International Symposium on Embedded
Multicore/Many-core Systems-on-Chip (MCSoC 2024)

December 2024

2023 IEEE 16th International Symposium on Embedded
Multicore/Many-core Systems-on-Chip (MCSoC 2023)

December 2023

6th Sustainable Computing Systems Workshop (SUSCW 23)

November 2023

5th Sustainable Computing Systems Workshop (SUSCW 22)

November 2022

4th Sustainable Computing Systems Workshop (SUSCW 21)

November 2021

Workshop on Challenges in Artificial Intelligence andMachine Learning for Internet of Things (AIChallengeIoT 2020)

November 2020

The University of Tokyo Nepali Society (UTNeS)
President (2017–2018)

2015–2021

Seminar Organizer
A Seminar on FPGA Technology and its role in Electronics
Engineering in Nepal
Sagarmatha Engineering College, Nepal
Refereed
Publications

July 2013

1. Sonnino, L., Shresthamali, S., He, Y., Kondo, M., (2024)
DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training andInference, (to be published)
2024 Design, Automation and Test in Europe Conference (DATE 2024)
2. Shresthamali, S., Kondo, M., (2023)
Enhancing Deep Reinforcement Learning with Compressed Sensing-based State Estimation,
2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip(MCSoC) (pp. 371-378).
3. Shresthamali, S., He, Y., Kondo, M., (2022)
FAWS: Fault-Aware Weight Scheduler for DNN Computations in Heterogeneous and Faulty
Hardware,2022 IEEE International Conference on Parallel and Distributed Processing with Applications(ISPA) (pp. 204-212).
4. Shresthamali, S., Kondo, M., Nakamura, H., (2022)
Multi-Objective Resource Scheduling for IoT Systems using Reinforcement Learning,
Journal of Low Power Electronics and Applications 12.4 (2022): 53.

Shaswot Shresthamali — Curriculum Vitae

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5. Shresthamali, S., Kondo, M., Nakamura, H., (2021)
Multi-objective Reinforcement Learning for Energy Harvesting Wireless Sensor Nodes,
2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip(MCSoC) (pp. 98-105).
6. Shresthamali, S., Kondo, M., Nakamura, H., (2019)
Power Management of Wireless Sensor Nodes with Coordinated Distributed Reinforcement
Learning,2019 IEEE 37th International Conference on Computer Design (ICCD) (pp. 638-647).
7. Shresthamali, S., Kondo, M., Nakamura, H., (2017)
Adaptive power management in solar energy harvesting sensor node using reinforcement learning,
ACM Transactions on Embedded Computing Systems (TECS), Vol. 16, Issue 5s, pp 1-21,
September, 2017.
8. Chhetri, S., R., Poudel, B., Ghimire, S., Shresthamali, S., Sharma, D., K., (2015)
Implementation of Audio Effect Generator in FPGA,
Nepal Journal of Science and Technology 2014, Vol. 15, Issue 1, pp. 89-98, December, 2014.
9. Shresthamali, S., (2014)
Parallel Processing Using FPGAs,
KEC Journal of Science and Engineering (KJSE), Vol. 2, Issue 1, pp 79-82, November, 2014.
UnrefereedPublications

1. Shresthamali, S., Kondo, M., (2022)
Enhancing Deep Reinforcement Learning with Compressed Sensing-based State Estimation,
Summer Workshop on Parallel/Distributed/Cooperative Processing (SWoPP 2023)
2. 杉本 寛直, シ ュ レ ス タマ リ サ ソ ット , 近藤 正章 (2023)
局所グラフ情報を用いた強化学習によるAGVの経路スケジューリング手法の検討 (A studyof reinforcement learning-based AGV route scheduling using local graph information),
第244回システム・アーキテクチャ研究発表会（ETNET2023）,
3. Shresthamali, S., He, Y., Kondo, M., (2022)
Fault-aware Hardware Scheduling of Computations in Deep Neural Networks ,Summer Workshop on Parallel/Distributed/Cooperative Processing (SWoPP 2022)
4. ソニーノ ロレンツォ, シュ レ ス タ マ リ サ ソ ット, 和 遠, 近藤 正章 (2022)
DNN推論高速化のためのSRAMベース近似デジタル乗算器の提案,
2022年並列／分散／協調処理に関するサマー・ワークショップ (SWoPP2022),
5. シ ュ レ ス タ マ リ サ ソ ット , 近藤 正章 , 中村 宏 (2017)
適応的電力制御を行う環境発電駆動センサノードの強化学習戦略の比較評価,
研究報告システム・アーキテクチャ（ARC）, Vol. 2017-ARC-227, No. 28, pp. 1-8, July,
2017.
6. シ ュ レ ス タ マ リ サ ソ ット , 近藤 正章 , 中村 宏 (2017)
強化学習を用いた環境発電駆動センサノードの適応的電力制御手法の検討,
研究報告システム・アーキテクチャ（ARC）, Vol. 2017-ARC-225, No. 26, pp. 1-6, March,
2017.

Presentations

1. Enhancing Deep Reinforcement Learning with Compressed Sensing-based State
Estimation,
2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip(MCSoC 2023), December 2023.
2. Enhancing Deep Reinforcement Learning with Compressed Sensing-based State
Estimation ,Summer Workshop on Parallel/Distributed/Cooperative Processing (SWoPP 2023), August2023

Shaswot Shresthamali — Curriculum Vitae

5 of 5

3. FAWS: Fault-Aware Weight Scheduler for DNN Computations in Heterogeneousand Faulty Hardware ,2022 IEEE International Conference on Parallel and Distributed Processing with Applications(ISPA 2022), December 2022
4. Fault-aware Hardware Scheduling of Computations in Deep Neural Networks ,Summer Workshop on Parallel/Distributed/Cooperative Processing (SWoPP 2022), July 2022
5. Multi-objective Reinforcement Learning for Energy Harvesting Wireless Sensor
Nodes,
2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip(MCSoC 2021), December 2021
6. Reinforcement Learning for Energy Harvesting Wireless Sensor Nodes,
Keynote Speech at 4th Sustainable Computing Systems Workshop (SUSCW 21),
December 2021
7. Power Management of Wireless Sensor Nodes with Coordinated Distributed Reinforcement Learning,2019 IEEE 37th International Conference on Computer Design (ICCD 2019), November 2019
8. Adaptive power management in solar energy harvesting sensor node using reinforcement learning,2017 International Conference on Embedded Software (EMSOFT), October 2017
9. Adaptive Power Management of Energy Harvesting Sensor Nodes using Reinforcement Learning: A comparison of Q-Learning and SARSA Algorithms,
Summer Workshop on Parallel/Distributed/Cooperative Processing (SWoPP 2017), July 2017
10. Reinforcement Learning For Power Management In Energy Harvesting Sensor
Nodes (Poster),
54th Design Automation Conference (DAC 2017), June 2017
11. Adaptive Power Management For Energy Harvesting Sensor Nodes,
217th IPSJ (ETNET 2017), March 2017
12. FPGA: A Brief Introduction,
Seminar on FPGA Technology And Its Role In Electronics Engineering In Nepal, Sagarmatha
Engineering College, July 2013
13. An Introduction to Parallel Processing Using FPGAs,LOCUS, Pulchowk Campus, June 2013