activity
20192021
most citedPartition Pruning: Parallelization-Aware Pruning for Deep Neural Networks

6 citations · 6 across the 1 of their papers we have counts for

collaborators

5 papers

cs.LG2021

PLAM: a Posit Logarithm-Approximate Multiplier

Raul Murillo, Alberto A. Del Barrio, Guillermo Botella +3

The Posit Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in Neural Network related tasks a…

cs.LG2020

The Effects of Approximate Multiplication on Convolutional Neural Networks

Min Soo Kim, Alberto A. Del Barrio, HyunJin Kim +1

This paper analyzes the effects of approximate multiplication when performing inferences on deep convolutional neural networks (CNNs). The approximate multiplication can reduce the…

cs.LG2020

Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators

Masoomeh Jasemi, Shaahin Hessabi, Nader Bagherzadeh

We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insigh…

physics.app-ph2019

Immunity of nanoscale magnetic tunnel junctions to ionizing radiation

Eric Arturo Montoya, Jen-Ru Chen, Randy Ngelale +9

Spin transfer torque magnetic random access memory (STT-MRAM) is a promising candidate for next generation memory as it is non-volatile, fast, and has unlimited endurance. Another…

cs.CV20196 cited

Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks

Sina Shahhosseini, Ahmad Albaqsami, Masoomeh Jasemi +1

Parameters of recent neural networks require a huge amount of memory. These parameters are used by neural networks to perform machine learning tasks when processing inputs. To spee…