Area of research
Electrical and Electronic Engineering · Computer Vision and Pattern Recognition
Research interest
Research interests include Advanced Memory and Neural Computing, Ferroelectric and Negative Capacitance Devices, Advanced Neural Network Applications, and Parallel Computing and Optimization Techniques.
Transitive Array: An Efficient GEMM Accelerator with Result Reuse
NDSEARCH: Accelerating Graph-Traversal-Based Approximate Nearest Neighbor Search through Near Data Processing
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function
Log-Likelihood Ratio Test for Spectrum Sensing With Truncated Covariance Matrix
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
An Overview of Hardware Security and Trust: Threats, Countermeasures, and Design Tools
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning
TernGrad: ternary gradients to reduce communication in distributed deep learning
arXiv (Cornell University) 2017cited by 396position: last
Compressive sensing-based wind speed estimation for low-altitude wind-shear with airborne phased array radar
LED-based digital hologram reconstruction by compressive sensing
Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
On-chip caches built on multilevel spin-transfer torque RAM cells and its optimizations
Hardware realization of BSB recall function using memristor crossbar arrays
Probabilistic design methodology to improve run-time stability and performance of STT-RAM caches
Memristor crossbar based hardware realization of BSB recall function
Non-volatile 3D stacking RRAM-based FPGA
Memristor-based synapse design and training scheme for neuromorphic computing architecture
STT-RAM Cell Design Considering CMOS and MTJ Temperature Dependence
The 3-D Stacking Bipolar RRAM for High Density
Statistical memristor modeling and case study in neuromorphic computing
A dual-mode architecture for fast-switching STT-RAM
Conference: NSF Workshop on Hardware-Software Co-design for Neuro-Symbolic Computation
CCF Core: Small: Hardware/Software Co-Design for Sustainability at the Edge
NSF Convergence Accelerator Track D: A Trusted Integrative Model and Data Sharing Platform for Accelerating AI-Driven Health Innovation
Collaborative Research: CNS Core: Medium: Exploiting Synergies Between Machine-Learning Algorithms and Hardware Heterogeneity for High-Performance and Reliable Manycore Computing
FET: Small: RESONANCE: Accelerating Speech/Language Processing through Collective Training using Commodity ReRAM Chips
SHF: Small: Cross-Platform Solutions for Pruning and Accelerating Neural Network Models
XPS: DSD: Collaborative Research: NeoNexus: The Next-generation Information Processing System across Digital and Neuromorphic Computing Domains
CSR: Small: Collaborative Research: GAMBIT: Efficient Graph Processing on a Memristor-based Embedded Computing Platform
SHF: Small: Cross-Platform Solutions for Pruning and Accelerating Neural Network Models
XPS: DSD: Collaborative Research: NeoNexus: The Next-generation Information Processing System across Digital and Neuromorphic Computing Domains
Collaborative Research: SMURFS: Statistical Modeling, SimUlation and Robust Design Techniques For MemriStors
CSR: Small: Collaborative Research: Cross-Layer Design Techniques for Robustness of the Next-Generation Nonvolatile Memories
Collaborative Research: SMURFS: Statistical Modeling, SimUlation and Robust Design Techniques For MemriStors
CAREER: STT-RAM based Memory Hierarchy and Management in Embedded Systems
CSR: Small: Collaborative Research: Cross-Layer Design Techniques for Robustness of the Next-Generation Nonvolatile Memories