2 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
Look-Up Table based Neural Network Hardware
Ovishake Sen, Chukwufumnanya Ogbogu, Peyman Dehghanzadeh +4
Traditional digital implementations of neural accelerators are limited by high power and area overheads, while analog and non-CMOS implementations suffer from noise, device mismatc…
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,…