79 citations · 208 across the 29 of their papers we have counts for
5 papers · 1 filter
MFIT: Multi-Fidelity Thermal Modeling for 2.5D and 3D Multi-Chiplet Architectures
Lukas Pfromm, Alish Kanani, Harsh Sharma +6
Rapidly evolving artificial intelligence and machine learning applications require ever-increasing computational capabilities, while monolithic 2D design technologies approach thei…
HeTraX: Energy Efficient 3D Heterogeneous Manycore Architecture for Transformer Acceleration
Pratyush Dhingra, Janardhan Rao Doppa, Partha Pratim Pande
Transformers have revolutionized deep learning and generative modeling to enable unprecedented advancements in natural language processing tasks and beyond. However, designing hard…
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…
Dataflow-Aware PIM-Enabled Manycore Architecture for Deep Learning Workloads
Harsh Sharma, Gaurav Narang, Janardhan Rao Doppa +2
Processing-in-memory (PIM) has emerged as an enabler for the energy-efficient and high-performance acceleration of deep learning (DL) workloads. Resistive random-access memory (ReR…
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,…