252 citations · 802 across the 19 of their papers we have counts for
19 papers
Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity
Hugo Latapie, Shan Yu, Patrick Hammer +14
Traditional computer vision models often necessitate extensive data acquisition, annotation, and validation. These models frequently struggle in real-world applications, resulting…
Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems
Debopam Sanyal, Jui-Tse Hung, Manav Agrawal +6
Model-serving systems have become increasingly popular, especially in real-time web applications. In such systems, users send queries to the server and specify the desired performa…
Subgraph Stationary Hardware-Software Inference Co-Design
Payman Behnam, Jianming Tong, Alind Khare +6
A growing number of applications depend on Machine Learning (ML) functionality and benefits from both higher quality ML predictions and better timeliness (latency) at the same time…
Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
Amey Agrawal, Sameer Reddy, Satwik Bhattamishra +4
With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rise…
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference
Alind Khare, Animesh Agrawal, Aditya Annavajjala +4
Neural Architecture Search (NAS) for Federated Learning (FL) is an emerging field. It automates the design and training of Deep Neural Networks (DNNs) when data cannot be centraliz…
Signed Binary Weight Networks
Sachit Kuhar, Alexey Tumanov, Judy Hoffman
Efficient inference of Deep Neural Networks (DNNs) is essential to making AI ubiquitous. Two important algorithmic techniques have shown promise for enabling efficient inference -…
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
Yanbo Xu, Alind Khare, Glenn Matlin +4
Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to de…
Automatic Parallelization of Python Programs for Distributed Heterogeneous Computing
Jun Shirako, Akihiro Hayashi, Sri Raj Paul +2
This paper introduces a novel approach to automatic ahead-of-time (AOT) parallelization and optimization of sequential Python programs for execution on distributed heterogeneous pl…
CompOFA: Compound Once-For-All Networks for Faster Multi-Platform Deployment
Manas Sahni, Shreya Varshini, Alind Khare +1
The emergence of CNNs in mainstream deployment has necessitated methods to design and train efficient architectures tailored to maximize the accuracy under diverse hardware & laten…
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
Shenda Hong, Yanbo Xu, Alind Khare +5
Deep learning models have achieved expert-level performance in healthcare with an exclusive focus on training accurate models. However, in many clinical environments such as intens…
HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline
Richard Liaw, Romil Bhardwaj, Lisa Dunlap +4
Prior research in resource scheduling for machine learning training workloads has largely focused on minimizing job completion times. Commonly, these model training workloads colle…
Cloudburst: Stateful Functions-as-a-Service
Vikram Sreekanti, Chenggang Wu, Xiayue Charles Lin +5
Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular. Current FaaS offerings are targeted at stateless functions that do minima…
The OoO VLIW JIT Compiler for GPU Inference
Paras Jain, Xiangxi Mo, Ajay Jain +3
Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e.g., GPUs, TPUs) point to alarmingly low utilization. As ML inference is increasingly time-bound…
Dynamic Space-Time Scheduling for GPU Inference
Paras Jain, Xiangxi Mo, Ajay Jain +5
Serving deep neural networks in latency critical interactive settings often requires GPU acceleration. However, the small batch sizes typical in online inference results in poor GP…
Serverless Computing: One Step Forward, Two Steps Back
Joseph M. Hellerstein, Jose Faleiro, Joseph E. Gonzalez +4
Serverless computing offers the potential to program the cloud in an autoscaling, pay-as-you go manner. In this paper we address critical gaps in first-generation serverless comput…
InferLine: ML Prediction Pipeline Provisioning and Management for Tight Latency Objectives
Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar +4
Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet…
Ray: A Distributed Framework for Emerging AI Applications
Philipp Moritz, Robert Nishihara, Stephanie Wang +8
The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requir…
IDK Cascades: Fast Deep Learning by Learning not to Overthink
Xin Wang, Yujia Luo, Daniel Crankshaw +3
Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that f…
Real-Time Machine Learning: The Missing Pieces
Robert Nishihara, Philipp Moritz, Stephanie Wang +7
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dyn…