Search Results for author:
Found 5 papers, 1 papers with code
LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices
Second, an optimized data transmission mechanism ensures efficient and structured data flow between model segments while also maintaining the integrity of the original model structure.
CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems
We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments which includes computer systems.
Plume: A Framework for High Performance Deep RL Network Controllers via Prioritized Trace Sampling
First, we identify the critical features that determine the behavior of the traces.
Caramel: Accelerating Decentralized Distributed Deep Learning with Computation Scheduling
The method of choice for parameter aggregation in Deep Neural Network (DNN) training, a network-intensive task, is shifting from the Parameter Server model to decentralized aggregation schemes (AllReduce) inspired by theoretical guarantees of better performance.
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
We develop a system, TicTac, to improve the iteration time by fixing this issue in distributed deep learning with Parameter Servers while guaranteeing near-optimal overlap of communication and computation.
