362 citations · 390 across the 7 of their papers we have counts for
10 papers
Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration
Reena Elangovan, Shubham Jain, Anand Raghunathan
Precision scaling has emerged as a popular technique to optimize the compute and storage requirements of Deep Neural Networks (DNNs). Efforts toward creating ultra-low-precision (s…
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems
Sourjya Roy, Shrihari Sridharan, Shubham Jain +1
Resistive crossbars have attracted significant interest in the design of Deep Neural Network (DNN) accelerators due to their ability to natively execute massively parallel vector-m…
Valley-Coupled-Spintronic Non-Volatile Memories with Compute-In-Memory Support
Sandeep Thirumala, Yi-Tse Hung, Shubham Jain +6
In this work, we propose valley-coupled spin-hall memories (VSH-MRAMs) based on monolayer WSe2. The key features of the proposed memories are (a) the ability to switch magnets with…
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks
Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs). In the quest for lower precis…
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
Shubham Jain, Abhronil Sengupta, Kaushik Roy +1
Resistive crossbars designed with non-volatile memory devices have emerged as promising building blocks for Deep Neural Network (DNN) hardware, due to their ability to compactly an…
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks
Sanchari Sen, Shubham Jain, Swagath Venkataramani +1
Deep Neural Networks (DNNs) have emerged as the method of choice for solving a wide range of machine learning tasks. The enormous computational demands posed by DNNs have most comm…
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
Jay Patravali, Shubham Jain, Sasank Chilamkurthy
In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are traine…
Computing in Memory with Spin-Transfer Torque Magnetic RAM
Shubham Jain, Ashish Ranjan, Kaushik Roy +1
In-memory computing is a promising approach to addressing the processor-memory data transfer bottleneck in computing systems. We propose Spin-Transfer Torque Compute-in-Memory (STT…
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
Soroush Mehri, Kundan Kumar, Ishaan Gulrajani +5
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memor…
A customized flocking algorithm for swarms of sensors tracking a swarm of targets
Anupam Shukla, Gaurav Ojha, Sachin Acharya +1
Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multi-modal sensing capabilities that communicate over wireless networks. WMSNs have more flexibili…