activity
20182022
most citedPerformance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks

64 citations · 99 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CV20222 cited

Motif Mining: Finding and Summarizing Remixed Image Content

William Theisen, Daniel Gonzalez Cedre, Zachariah Carmichael +3

On the internet, images are no longer static; they have become dynamic content. Thanks to the availability of smartphones with cameras and easy-to-use editing software, images can…

cs.SE2021

Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems

Sophia Abraham, Zachariah Carmichael, Sreya Banerjee +5

Computer vision approaches are widely used by autonomous robotic systems to sense the world around them and to guide their decision making as they perform diverse tasks such as col…

q-bio.PE202033 cited

SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread

Nicholas Soures, David Chambers, Zachariah Carmichael +5

The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and precipitated varying policies to effectuate physical distancing to ameliorate its impact. In this study,…

cs.LG2019

Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge

Hamed F. Langroudi, Zachariah Carmichael, David Pastuch +1

Low-precision DNNs have been extensively explored in order to reduce the size of DNN models for edge devices. Recently, the posit numerical format has shown promise for DNN data re…

cs.LG2019

Deep Learning Training on the Edge with Low-Precision Posits

Hamed F. Langroudi, Zachariah Carmichael, Dhireesha Kudithipudi

Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision ([5..8]-bit). However, majority of studies focus only on DNN…

eess.SP2019

Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting

Zachariah Carmichael, Humza Syed, Dhireesha Kudithipudi

Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown…