Sandeep Chinchali

TL;DR: This work presents a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic and can enable mobile networks to carry 14.7% more data with minimal impact on existing traffic.

TL;DR: In this paper, the authors present xFabric, a datacenter transport design that provides flexible and fast bandwidth allocation control, which enables operators to specify how bandwidth is allocated among contending flows to optimize for different service level objectives such as minimizing flow completion times, weighted allocations, different notions of fairness, etc.

TL;DR: In this article, the authors present xFabric, a datacenter transport design that provides flexible and fast bandwidth allocation control, which enables operators to specify how bandwidth is allocated among contending flows to optimize for different service level objectives such as minimizing flow completion times, weighted allocations, different notions of fairness, etc.

TL;DR: This work proposes a distributed DNN architecture that learns end-to-end how to represent the raw sensor data and send it over the network such that it meets the eventual sensing task's needs.

TL;DR: This paper formulates offloading as a sequential decision making problem for robots, and proposes a solution using deep reinforcement learning, which improves vision task performance and allows robots the potential to significantly transcend their on-board sensing accuracy but with limited cost of cloud communication.