131 citations · 139 across the 3 of their papers we have counts for
3 papers
MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks
Nesma M. Rezk, Tomas Nordström, Dimitrios Stathis +3
The compression of deep learning models is of fundamental importance in deploying such models to edge devices. The selection of compression parameters can be automated to meet chan…
Recurrent Neural Networks: An Embedded Computing Perspective
Nesma M. Rezk, Madhura Purnaprajna, Tomas Nordström +1
Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest i…
Kickstarting High-performance Energy-efficient Manycore Architectures with Epiphany
Andreas Olofsson, Tomas Nordström, Zain Ul-Abdin
In this paper we introduce Epiphany as a high-performance energy-efficient manycore architecture suitable for real-time embedded systems. This scalable architecture supports floati…