362 citations · 367 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
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…