64 citations · 94 across the 4 of their papers we have counts for
4 papers
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT
Hamed F. Langroudi, Vedant Karia, Tej Pandit +1
In this research, we propose a new low-precision framework, TENT, to leverage the benefits of a tapered fixed-point numerical format in TinyML models. We introduce a tapered fixed-…
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…
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,…