65 citations · 78 across the 9 of their papers we have counts for
9 papers
Building Reliable Arithmetic Multipliers Under NBTI Aging and Process Variations
Masoud Heidary, Biresh Kumar Joardar
Hardware aging poses a significant challenge for integrated circuits (ICs), leading to performance degradation and eventual failure. In this work, we focus on the aging of arithmet…
Attacking AI Accelerators by Leveraging Arithmetic Properties of Addition
Masoud Heidary, Biresh Kumar Joardar
The dependability of AI models relies largely on the reliability of the underlying computation hardware. Hardware aging attacks can compromise the computing substrate and disrupt A…
HePGA: A Heterogeneous Processing-in-Memory based GNN Training Accelerator
Chukwufumnanya Ogbogu, Gaurav Narang, Biresh Kumar Joardar +3
Processing-In-Memory (PIM) architectures offer a promising approach to accelerate Graph Neural Network (GNN) training and inference. However, various PIM devices such as ReRAM, FeF…
FARe: Fault-Aware GNN Training on ReRAM-based PIM Accelerators
Pratyush Dhingra, Chukwufumnanya Ogbogu, Biresh Kumar Joardar +3
Resistive random-access memory (ReRAM)-based processing-in-memory (PIM) architecture is an attractive solution for training Graph Neural Networks (GNNs) on edge platforms. However,…
ReaLPrune: ReRAM Crossbar-aware Lottery Ticket Pruned CNNs
Biresh Kumar Joardar, Janardhan Rao Doppa, Hai Li +2
Training machine learning (ML) models at the edge (on-chip training on end user devices) can address many pressing challenges including data privacy/security, increase the accessib…
Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise
Xiaoxuan Yang, Syrine Belakaria, Biresh Kumar Joardar +5
Resistive random-access memory (ReRAM) is a promising technology for designing hardware accelerators for deep neural network (DNN) inferencing. However, stochastic noise in ReRAM c…
ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks
Aqeeb Iqbal Arka, Biresh Kumar Joardar, Janardhan Rao Doppa +2
Graph Neural Network (GNN) is a variant of Deep Neural Networks (DNNs) operating on graphs. However, GNNs are more complex compared to traditional DNNs as they simultaneously exhib…
HeM3D: Heterogeneous Manycore Architecture Based on Monolithic 3D Vertical Integration
Aqeeb Iqbal Arka, Biresh Kumar Joardar, Ryan Gary Kim +3
Heterogeneous manycore architectures are the key to efficiently execute compute- and data-intensive applications. Through silicon via (TSV)-based 3D manycore system is a promising…
Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems
Biresh Kumar Joardar, Ryan Gary Kim, Janardhan Rao Doppa +3
The rising use of deep learning and other big-data algorithms has led to an increasing demand for hardware platforms that are computationally powerful, yet energy-efficient. Due to…