79 citations · 208 across the 29 of their papers we have counts for
5 papers · 1 filter
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…
Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach
Aryan Deshwal, Syrine Belakaria, Ganapati Bhat +2
Mobile system-on-chips (SoCs) are growing in their complexity and heterogeneity (e.g., Arm's Big-Little architecture) to meet the needs of emerging applications, including games an…
SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms
Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa, Partha Pratim Pande
We consider the task of photo-realistic unconditional image generation (generate high quality, diverse samples that carry the same visual content as the image) on mobile platforms…
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…