13 citations · 32 across the 5 of their papers we have counts for
5 papers
An Analytical Cost Model for Fast Evaluation of Multiple Compute-Engine CNN Accelerators
Fareed Qararyah, Mohammad Ali Maleki, Pedro Trancoso
Convolutional Neural Networks (CNNs) serve various applications with diverse performance and resource requirements. Model-aware CNN accelerators best address these diverse requirem…
Fusing Depthwise and Pointwise Convolutions for Efficient Inference on GPUs
Fareed Qararyah, Muhammad Waqar Azhar, Mohammad Ali Maleki +1
Depthwise and pointwise convolutions have fewer parameters and perform fewer operations than standard convolutions. As a result, they have become increasingly used in various compa…
VEDLIoT -- Next generation accelerated AIoT systems and applications
Kevin Mika, René Griessl, Nils Kucza +29
The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propo…
VEDLIoT: Very Efficient Deep Learning in IoT
Martin Kaiser, Rene Griessl, Nils Kucza +33
The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also deali…
A Computational-Graph Partitioning Method for Training Memory-Constrained DNNs
Fareed Qararyah, Mohamed Wahib, Doğa Dikbayır +2
Many state-of-the-art Deep Neural Networks (DNNs) have substantial memory requirements. Limited device memory becomes a bottleneck when training those models. We propose ParDNN, an…