activity
20192026
most citedA Single-MOSFET MAC for Confidence and Resolution (CORE) Driven Machine Learning Classification

3 citations · 4 across the 7 of their papers we have counts for

collaborators

12 papers

eess.SY2026

Experimental Characterization of SMD Inductors for Cryogenic Power Conversion at 4 K

Rami Rasheedi, Umeshkumar Patel, Jonathan Baldwin +2

With the increasing complexity and scalability demands of cryogenic electronic systems, such as quantum computing platforms, conversion of power within the cryogenic environment ha…

physics.ins-det2026

Electrothermal behavior of superconducting nanowires buried in a commercial CMOS process

Mohamed Gharib, Leonid Popryho, Salma Abdelzaher +14

Superconducting films native to a commercial CMOS process enable co-integration of superconducting devices (high-kinetic-inductance elements and nanowire detectors) with cryogenic…

cs.LG2026

ADVERSARIAL: And-Inverter Graph-Assisted Hardware Trojan Detection At Scale

Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods impractical due to their immens…

eess.SY2026

A Comprehensive Design Framework for Vertical Power Delivery in High-Performance Computing

Sriharini Krishnakumar, Yaroslav Popryho, Mingeun Choi +4

Power delivery -- including high-to-low voltage conversion, complex power distribution across heterogeneously integrated chiplets, and efficient interconnect allocation -- remains…

eess.SY2026

Power-Integrity Modeling of VR Faults in High-Performance Applications

Sriharini Krishnakumar, Inna Partin-Vaisband

Distributed vertical power delivery has emerged as a promising approach to meet aggressive current-density, efficiency, and transient response requirements in high-performance comp…

eess.SY2026

Dynamic Power Management Methodology for Distributed Vertical Power Delivery in High-Performance Computing Systems

Sriharini Krishnakumar, Inna Partin-Vaisband

Distributed vertical power delivery (DVPD) architectures employ multiple parallel voltage regulators (VRs) to meet the high-power and high current density demands of modern high pe…

cs.CR2025

Automated Hardware Trojan Insertion in Industrial-Scale Designs

Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

Industrial Systems-on-Chips (SoCs) often comprise hundreds of thousands to millions of nets and millions to tens of millions of connectivity edges, making empirical evaluation of h…

eess.SY2023

Vertical Power Delivery for Emerging Packaging and Integration Platforms -- Power Conversion and Distribution

Sriharini Krishnakumar, Dr. Inna Partin-Vaisband

Efficient delivery of current from PCB to point-of-load (POL) is a primary concern in modern high-power high-density integrated systems. Traditionally, a 48 V power signal is conve…

cs.AR2020

A Machine Learning Pipeline Stage for Adaptive Frequency Adjustment

Arash Fouman Ajirlou, Inna Partin-Vaisband

A machine learning (ML) design framework is proposed for adaptively adjusting clock frequency based on propagation delay of individual instructions. A random forest model is traine…

cs.CV2019★ 1 cited

Progressive VAE Training on Highly Sparse and Imbalanced Data

Dmitry Utyamishev, Inna Partin-Vaisband

In this paper, we present a novel approach for training a Variational Autoencoder (VAE) on a highly imbalanced data set. The proposed training of a high-resolution VAE model begins…

cs.ET2019

Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification

Farid Kenarangi, Xuan Hu, Yihan Liu +3

Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems…

eess.SP2019★ 3 cited

A Single-MOSFET MAC for Confidence and Resolution (CORE) Driven Machine Learning Classification

Farid Kenarangi, Inna Partin-Vaisband

Mixed-signal machine-learning classification has recently been demonstrated as an efficient alternative for classification with power expensive digital circuits. In this paper, a h…