EDBT 2026 Demo / reviewers in the wild / expert
Amit Chhabra
dblp:98/2203
· DBLP profile ↗
14ranked-venue papers
4as first author
8since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 8 · 4 first-author · 3 since 2021Artificial intelligence and machine learning · 3 · 3 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Predicting software defects using an extreme gradient boosting model tuned with reinforcement learning based spider wasp optimizer
Raja Oueslati, Mohamed Wajdi Ouertani, Ghaith Manita, Amit Chhabra |
Autom. Softw. Eng. | 4 |
| 2025 | A Halton enhanced solution-based Human Evolutionary Algorithm for complex optimization and advanced feature selection problems
Mahmoud Abdel-Salam, Amit Chhabra, Malik Braik, Farhad Soleimanian Gharehchopogh, Nebojsa Bacanin |
Knowl. Based Syst. | 2 |
| 2024 | Improving performance of extreme learning machine for classification challenges by modified firefly algorithm and validation on medical benchmark datasets
Nebojsa Bacanin, Catalin Stoean, Dusan Markovic, Miodrag Zivkovic, Tarik A. Rashid, Amit Chhabra, Marko Sarac |
Multim. Tools Appl. | 6 |
| 2024 | Boosting white shark optimizer for global optimization and cloud scheduling problem
Reham R. Mostafa, Amit Chhabra, Ahmed Khedr 0001, Fatma A. Hashim |
Neural Comput. Appl. | 2 |
| 2023 | Energy-aware workflow scheduling in fog computing using a hybrid chaotic algorithm
Ali Mohammadzadeh, Mahdi Akbari Zarkesh, Pouria Haji Shahmohamd, Javid Akhavan, Amit Chhabra |
J. Supercomput. | 5 |
| 2023 | Awareness requirement and performance management for adaptive systems: a survey
Tarik A. Rashid, Bryar Ahmad Hassan, Abeer Alsadoon, Shko Muhammed Qader, S. Vimal 0001, Amit Chhabra, Zaher Mundher Yaseen |
J. Supercomput. | 6 |
| 2022 | Harmony search: Current studies and uses on healthcare systems
Maryam T. Abdulkhaleq, Tarik A. Rashid, Abeer Alsadoon, Bryar Ahmad Hassan, Mokhtar Mohammadi, Jaza Mahmood Abdullah, Amit Chhabra, Sazan L. Ali, Rawshan N. Othman, Hadil A. Hasan, Sara Azad, Naz A. Mahmood, Sivan S. Abdalrahman, Hezha O. Rasul, Nebojsa Bacanin, S. Vimal 0001 |
Artif. Intell. Medicine | 7 |
| 2022 | Optimizing bag-of-tasks scheduling on cloud data centers using hybrid swarm-intelligence meta-heuristic
Amit Chhabra, Kuo-Chan Huang, Nebojsa Bacanin, Tarik A. Rashid |
J. Supercomput. | 1 |
| 2020 | Two-level utilization-based processor allocation for scheduling moldable jobs
Ying-Jhih Wu, Shuo-Ting Yu, Kuan-Chou Lai, Amit Chhabra, Hsi-Ya Chang, Kuo-Chan Huang |
J. Supercomput. | 4 |
| 2019 | Automated employee evaluation using fuzzy and neural network synergism through IoT assistance
Keshav Dhir, Amit Chhabra |
Pers. Ubiquitous Comput. | 2 |
| 2016 | Temperature-based adaptive memory sub-system in 28nm UTBB FDSOIabstractTemperature plays a crucial role in deciding SRAM performance especially at very low voltage. SRAM bitcell has conflicting constraints of write-ability and stability at cold (-40°C typically) and hot temperature (125°C typically) respectively. In order to reduce SRAM minimum operating voltage (VMIN), write and stability assist schemes are deployed. Fully-Depleted SOI (FDSOI) technology offers single P-well SRAM bitcell, where a single voltage can be used to adjust the state of the body (or P-well) of all bitcell devices. This voltage can vary from -1.1V to +1.1V. In this paper, we present detailed architecture and implementation of the low-power adaptive memory sub-system that modulates body bias voltage based on the junction temperature to reduce SRAM VMIN. The body is biased to positive voltages up to +1.1V to boost write-ability at cold temperatures and up to -1.1V to boost stability at hot temperatures. In addition, the system selects appropriate assist scheme based on temperature information. We present the dynamic simulation based functional verification environment using temperature and voltage-aware memory models that are compliant with IEEE 1801. We gained 50mV in SRAM VMIN thereby allowing 0.55V operation using high density 0.120μm2 single P-well bitcell in 28nm planar Ultra-Thin Box and Body (UTBB) FDSOI CMOS technology. We also gained 18% dynamic power during regular temperature range of 10°C and 75°C. In addition, due to forward body bias at cold temperature, we gained 30% in SRAM access time. Amit Chhabra, Mudit Srivastava, Prakhar Raj Gupta, Kedar Janardan Dhori, Philippe Triolet, Thierry Di Gilio, Nitin Bansal, B. Sujatha |
ISCAS | 1 |
| 2016 | Low-Energy Power-ON-Reset Circuit for Dual Supply SRAMabstractDesign of a low-energy power-ON reset (POR) circuit is proposed to reduce the energy consumed by the stable supply of the dual supply static random access memory (SRAM), as the other supply is ramping up. The proposed POR circuit, when embedded inside dual supply SRAM, removes its ramp-up constraints related to voltage sequencing and pin states. The circuit consumes negligible energy during ramp-up, does not consume dynamic power during operations, and includes hysteresis to improve noise immunity against voltage fluctuations on the power supply. The POR circuit, designed in the 40-nm CMOS technology within 10.6-μm2area, enabled 27× reduction in the energy consumed by the SRAM array supply during periphery power-up in typical conditions. Amit Chhabra, Yagnesh Dineshbhai Vaderiya |
IEEE Trans. Very Large Scale Integr. Syst. | 1 |
| 2015 | FALPEM: Framework for Architectural-Level Power Estimation and Optimization for Large Memory Sub-SystemsabstractFramework is developed for estimation of power at pre register transfer level (RTL) stage for structured memory sub-systems. Power estimation model is proposed specifically targeting power consumed by clock network and interconnect. The model is validated with VCD-based simulation on back-annotated netlist of an 8 MB memory sub-system used as video RAM (VRAM) for high-end graphics applications. This methodology also forms the basis for low-power exploration driving floor plan choice, gating structure of data, and clock network. We demonstrate 57% reduction in dynamic power by using low-power techniques for the 8 MB VRAM used as frame buffer in a graphics processor. FALPEM can be extended to other applications like processor cache and ASIC designs. Amit Chhabra, Harsh Rawat, Pascal Tessier, Daniel Pierredon, Laurent Bergher, Promod Kumar |
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. | 1 |
| 2006 | A Software Architecture Framework for On-Line Option Pricing
Kiran Kola, Amit Chhabra, Ruppa K. Thulasiram, Parimala Thulasiraman |
ISPA | 2 |