4 citations · 15 across the 15 of their papers we have counts for
15 papers
KLAS: Using Similarity to Stitch Neural Networks for Improved Accuracy-Efficiency Tradeoffs
Debopam Sanyal, Anantharaman Iyer, Alind Khare +5
Given the wide range of deployment targets, flexible model selection is essential for optimizing performance within a given compute budget. Recent work demonstrates that stitching…
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
Shraddha Barke, Arnav Goyal, Alind Khare +3
AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy tool outputs. We address this gap…
Sutradhara: An Intelligent Orchestrator-Engine Co-design for Tool-based Agentic Inference
Anish Biswas, Kanishk Goel, Srivarshinee S +5
Agentic applications are LLMs that iteratively invoke external tools to accomplish complex tasks. Such tool-based agents are rapidly becoming the dominant paradigm for deploying la…
Serving Heterogeneous LoRA Adapters in Distributed LLM Inference Systems
Shashwat Jaiswal, Shrikara Arun, Anjaly Parayil +8
Low-Rank Adaptation (LoRA) has become the de facto method for parameter-efficient fine-tuning of large language models (LLMs), enabling rapid adaptation to diverse domains. In prod…
ModServe: Modality- and Stage-Aware Resource Disaggregation for Scalable Multimodal Model Serving
Haoran Qiu, Anish Biswas, Zihan Zhao +9
Large multimodal models (LMMs) demonstrate impressive capabilities in understanding images, videos, and audio beyond text. However, efficiently serving LMMs in production environme…
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training
Aditya Annavajjala, Alind Khare, Animesh Agrawal +4
CNNs are increasingly deployed across different hardware, dynamic environments, and low-power embedded devices. This has led to the design and training of CNN architectures with th…
SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads
Alind Khare, Dhruv Garg, Sukrit Kalra +3
The increasing deployment of ML models on the critical path of production applications in both datacenter and the edge requires ML inference serving systems to serve these models u…
STRIDE: Structure and Embedding Distillation with Attention for Graph Neural Networks
Anshul Ahluwalia, Payman Behnam, Rohit Das +4
Recent advancements in Graph Neural Networks (GNNs) have led to increased model sizes to enhance their capacity and accuracy. Such large models incur high memory usage, latency, an…
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…
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…
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…
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…
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…
A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs
Koyel Mukherjee, Alind Khare, Ashish Verma
Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime. Especially, for the cases of adversarial training o…