4 citations · 15 across the 15 of their papers we have counts for
5 papers · 1 filter
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…