27 citations · 58 across the 6 of their papers we have counts for
7 papers
Clear-Weighted Bit Allocation for Satellite Downlinks
Alireza Furutanpey, Qiyang Zhang, Yujie Huang +2
Earth-observation satellites capture more imagery than intermittent ground contacts can transmit. Onboard systems threshold a cloud detector, discard frames or tiles, and compress…
Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices
Boris Sedlak, Philipp Raith, Andrea Morichetta +2
Edge devices have limited resources, which inevitably leads to situations where stream processing services cannot satisfy their needs. While existing autoscaling mechanisms focus e…
Leveraging Neural Graph Compilers in Machine Learning Research for Edge-Cloud Systems
Alireza Furutanpey, Carmen Walser, Philipp Raith +2
This work presents a comprehensive evaluation of neural network graph compilers across heterogeneous hardware platforms, addressing the critical gap between theoretical optimizatio…
Towards Multi-dimensional Elasticity for Pervasive Stream Processing Services
Boris Sedlak, Andrea Morichetta, Philipp Raith +2
This paper proposes a hierarchical solution to scale streaming services across quality and resource dimensions. Modern scenarios, like smart cities, heavily rely on the continuous…
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Alireza Furutanpey, Qiyang Zhang, Philipp Raith +3
Nanosatellite constellations equipped with sensors capturing large geographic regions provide unprecedented opportunities for Earth observation. As constellation sizes increase, ne…
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
Alireza Furutanpey, Johanna Barzen, Marvin Bechtold +4
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work h…
FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing
Alireza Furutanpey, Philipp Raith, Schahram Dustdar
The rise of mobile AI accelerators allows latency-sensitive applications to execute lightweight Deep Neural Networks (DNNs) on the client side. However, critical applications requi…