In this advancing era of technology and innovation, semiconductor development has exploded, enabling researchers to develop cutting-edge, ultra-low-power, and high-speed integrated circuits. However, this scaling progress ...
Recent advancements in the semiconductor industry have paved the way for a broader use of semiconductor chips in various areas such as computing, data processing, and communication. Adders, which are the smallest and most ...
As environmental concerns rise, the industry faces pressure to reduce energy consumption. Research has been going on to design energy-efficient designs, particularly in low-power, high-performance applications. Flip-flops ...
This work presents a comprehensive study on the design, implementation, and sustainability assessment of a modified version of a Dickson charge pump circuit. The circuit is implemented to operate at 1.2 V and achieves an ...
In order to efficiently learn from small amount of labeled data, this study presents pseudo- labeling using semi-supervised learning in a federated setting (Pseudo-FedSSL), a novel approach to semi-supervised federated ...
Federated learning (FL) is a privacy-preserving machine learning approach that enables the training of models across multiple decentralized edge devices without exchanging raw data. However, local models trained only on ...
With the scaling of semiconductor technology nodes, the impact of process-induced variations has increased. Statistical static timing analysis accounts for global and local variations in the timing analysis. The present ...
Out-of-Distribution (OoD) detection has emerged as a crucial aspect in machine learning, essential for ensuring the resilience and reliability of models deployed in real-world scenarios. Traditional methods excel at ...
Embedded memories occupy up to 70% of the area and account for 30-50% power consumption in advanced digital SoCs. A large part of this power is leakage power. Therefore, high-density, low-leakage SRAM cells are desirable. ...
Gupta, Sagar; Subramanyam, A V (Advisor)(IIIT-Delhi, 2020-07-01)
Unsupervised Person Re-Identi cation (Re-ID) su ers severely from the gap in the modality. Many factors pose a challenge to the task, including occlusions, lightning conditions, pose changes, among several others. Various ...
The reproducibility of experiments has been a long-standing obstruction for farther scientific evolution. Computational methods are being involved to accelerate and to economize drug discovery and the development process. ...
Visual wildlife monitoring of animals requires detection for species-level categorization and re-identification (Re-ID) for population estimation of an individual species. Traditionally, the monitoring is done via GPS ...
Khan, Zuber; Prasad, Ranjitha (Advisor)(IIIT-Delhi, 2022-12)
Optimal treatment selection is extremely crucial in emergency situations such as for a patient admitted in ICU. However, the chosen medical treatment may not necessarily be a financially favorable choice. In some of the ...
Rapid beam alignment is required to support high gain millimeter wave (mmW) communication links between a base station (BS) and mobile users (MU). The standard IEEE 802.11ad protocol enables beam alignment at the BS ...
Machine learning (ML) models that accurately predict treatment effects and related healthcare costs can bring significant efficiencies in the healthcare industry. These models could help reduce fatalities resulting from ...
Next-generation wireless communication systems are expected to support high-mobility and high-bandwidth vehicle-to-everything (V2X) communications in sub-6 GHz and millimeter wave (mmWave) spectrum. The deployment in mmWave ...
Achieving timing closure is a challenging task, and it becomes more complicated due to the artificial pessimism in the traditional timing models of the flip-flops. During the signoff stages, we can alleviate this problem ...
An immense amount of data is generated daily usingmodern technologies in autonomous vehicles, IoT, smart grids, etc. But unfortunately, this data generated at the edge cannot be used for any machine learning model training ...