Search Results for author:
Found 9 papers, 2 papers with code
Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving
Machine learning (ML) inference platforms are tasked with balancing two competing goals: ensuring high throughput given many requests, and delivering low-latency responses to support interactive applications.
MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations
Camera orientations (i. e., rotation and zoom) govern the content that a camera captures in a given scene, which in turn heavily influences the accuracy of live video analytics pipelines.
Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection
Data augmentation has been rare in the cyber security domain due to technical difficulties in altering data in a manner that is semantically consistent with the original data.
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs
DNN models across many domains continue to grow in size, resulting in high resource requirements for effective training, and unpalatable (and often unaffordable) costs for organizations and research labs across scales.
GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth overheads and privacy violations, but in doing so, face an ever-growing resource tension.
Revelio: ML-Generated Debugging Queries for Distributed Systems
A major difficulty in debugging distributed systems lies in manually determining which of the many available debugging tools to use and how to query its logs.
Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics
Commercial retrospective video analytics platforms have increasingly adopted general interfaces to support the custom queries and convolutional neural networks (CNNs) that different applications require.
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
Computation separation makes it possible to construct a deep, bounded-asynchronous pipeline where graph and tensor parallel tasks can fully overlap, effectively hiding the network latency incurred by Lambdas.
Continuous Prefetch for Interactive Data Applications
Interactive data visualization and exploration (DVE) applications are often network-bottlenecked due to bursty request patterns, large response sizes, and heterogeneous deployments over a range of networks and devices.
Databases
