SOFTWARE

'Working Code Trumps All Hype'
Courtesy: Professor M. Satyanarayan, Carnegie Mellon University

SCISSIONTL

ScissionTL is a tool for automated benchmarking of distributed DNNs on a given set of target device, edge and cloud resources while minimising the data transfer using transfer layers for maximising the performance of DNN inference.

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SCISSION

Scission is a tool for automated benchmarking of distributed DNNs on a given set of target device, edge and cloud resources for identifying the execution approach and determining the optimal partition for maximising the performance of DNN inference.

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DEEP NEURAL NETWORK ADAPTIVITY

The software aims to identify whether there is a case for adaptivity - the need for repartitioning DNNs to adapt to changing operational conditions (CPU, memory, or network) at the network edge once it is already deployed across the cloud and the edge.

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DEFOG

DeFog is an open source Fog/Edge benchmarking suite comprising six benchmarks: (1) YOLO - deep learning-based object recognition, (2) PocketSphinx - text-to-speech converter, (3) Aeneas - text-to-audio forced alignment, (4) iPokeMon - geolocation based mobile game, (5) FogLAMP - IoT edge gateway application, and (6) RealFD - real-time face detection on video streams

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ENORM

ENORM is a framework for Edge NOde Resource Management. The framework realises an architecture and a few fundamental algorithms for managing Edge resources. The framework is demonstrated on a PokeMon Go-like online game.

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DYVERSE

DYVERSE (DYnamic VERtical Scaling in Multi-tenant Edge Environments) builds on ENORM while providing priority-based dynamic resource allocation for supporting multiple Edge applications on an individual Edge node. The research is demonstrated on multiple applications.

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EDGE FAIR SCHEDULER

The Edge is anticipated to be resource constrained and a competitive environment to acquire resources for services offloaded (jobs) from the Cloud (client) to the Edge. Edge Fair Scheduler (EFS) is a prototype for fairly distributing jobs on an individual Edge node while being fair to multiple clients and the priorities associated with the jobs.

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