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 ...

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 ...

As CMOS technology continues to scale, integrated circuits are increasingly susceptible to radiation-induced disruptions, necessitating resilient yet efficient hardening techniques. Conventional radiation-hardened flip-flops ...

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 ...

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 ...

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. ...

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 ...

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 ...

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 ...