2 citations · 2 across the 6 of their papers we have counts for
7 papers · 1 filter
ThAME: 3D Memory-Enabled Heterogeneous Accelerator for LLM Mixture of Experts
Pratyush Dhingra, Pramit Kumar Pal, Janardhan Rao Doppa +1
Mixture of Experts (MoE) architectures have emerged as a dominant paradigm for scaling Large Language Models (LLMs). However, MoE inference on conventional hardware is constrained…
ADEPT: Architecture-Driven Energy-Efficient CNN Fine-Tuning on PIM Accelerators
Pratyush Dhingra, Vibhanshu Sharma, Janardhan Rao Doppa +1
Processing-in-memory-based (PIM) architectures have emerged as a promising solution for accelerating Convolutional Neural Network (CNN) workloads at the edge. Fine-tuning pre-train…
ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures
Vibhanshu Sharma, Pratyush Dhingra, Janardhan Rao Doppa +1
Processing-In-Memory (PIM) has emerged as a promising technology for accelerating machine learning (ML) workloads. Specifically, non-volatile memory-based PIM architectures have en…
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures
Pratyush Dhingra, Janardhan Rao Doppa, Partha Pratim Pande
Transformer architectures have become the standard neural network model for various machine learning applications including natural language processing and computer vision. However…
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…
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,…