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The proliferation of video analytics in applications like autonomous driving, traffic surveillance, and teleoperated vehicles requires on-premise (on edge) execution of deep learning models to meet latency requirements and curb bandwidth usage by ...

With traffic surveillance increasingly used, thousands of cameras on roads send video feeds to cloud servers to run computer vision algorithms, requiring high bandwidth. State-of-the-art techniques reduce the bandwidth requirement by either sending a ...

Traditional end-host network stacks are struggling to keep up with rapidly increasing datacenter access link bandwidths due to their unsustainable CPU overheads. Motivated by this, our community is exploring a multitude of solutions for future network ...

We present Gandiva_fair, a distributed, fair share scheduler that balances conflicting goals of efficiency and fairness in GPU clusters for deep learning training (DLT). Gandiva_fair provides performance isolation between users, enabling multiple users to ...

This paper proposes a methodology to extract key insights from user generated reviews. This work is based on Aspect Based Sentiment Analysis (ABSA) which predicts the sentiment of aspects mentioned in the text documents. The extracted aspects are fine-...