64 citations · 316 across the 28 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Performance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks
Zachariah Carmichael, Hamed F. Langroudi, Char Khazanov +3
Deep neural networks (DNNs) have been demonstrated as effective prognostic models across various domains, e.g. natural language processing, computer vision, and genomics. However,…
Deep Positron: A Deep Neural Network Using the Posit Number System
Zachariah Carmichael, Hamed F. Langroudi, Char Khazanov +3
The recent surge of interest in Deep Neural Networks (DNNs) has led to increasingly complex networks that tax computational and memory resources. Many DNNs presently use 16-bit or…