1 citations · 3 across the 12 of their papers we have counts for
13 papers
Structure-augmented LLMs for High-Level Synthesis Pragma Optimization
Haocheng Xu, Ye Qiao, Phyo Pyae Moe Aung +7
Pragma insertion drives the quality of high-level synthesis (HLS) designs. Choosing the right directives demands expert knowledge and reasoning about loop nesting, data dependences…
LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs
Liad Gerstman, Aditya Dhakal, Dejan Milojicic +1
Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems. However, as graph sizes grow…
Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs
Tianyu Wang, Gourav Rattihalli, Aditya Dhakal +2
As LLM inference becomes a major cloud workload, its growing energy footprint makes cluster-wide energy optimization increasingly important. Serverless LLM serving helps platforms…
Predict, Reuse, and Repair: Accelerating Dynamic Sparse Attention for Long-Context LLM Decoding
Tianyu Wang, Gourav Rattihalli, Aditya Dhakal +4
Dynamic sparse attention (DSA) accelerates long-context LLM decoding by attending to only the top-K KV blocks relevant to each query, but it introduces a serialized selection-to-at…
ObjectCache: Layerwise Object-Storage Retrieval for KV Cache Reuse
Yu Zhu, Aditya Dhakal, Yunming Xiao +2
Prefix KV caching has become a key mechanism in LLM serving: it reduces time to first token (TTFT) by avoiding redundant computation across requests that share a prefix (i.e., the…
MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters
H. Moore, S. Qi, D. Milojicic +2
Large Language Models (LLMs) have become increasingly prevalent in cloud-based platforms, propelled by the introduction of AI-based consumer and enterprise services. LLM inference…
Sustainable Graph Analytics Workload Scheduling with Evolutionary Reinforcement Learning in Edge-Cloud Systems
P. Ramicetty, H. Moore, S. Qi +5
Graph analytics powers modern intelligent systems such as smart cities, cyber-physical infrastructure, IoT security, and large-scale social networks. As these workloads scale in co…
AI+HW 2035: Shaping the Next Decade
Deming Chen, Jason Cong, Azalia Mirhoseini +27
Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined. The global research community lacks…
An RDMA-First Object Storage System with SmartNIC Offload
Yu Zhu, Aditya Dhakal, Pedro Bruel +4
AI training and inference impose sustained, fine-grain I/O that stresses host-mediated, TCP-based storage paths. Motivated by kernel-bypass networking and user-space storage stacks…
Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters
Hayden Moore, Sirui Qi, Ninad Hogade +3
In recent years, Large Language Models (LLM) such as ChatGPT, CoPilot, and Gemini have been widely adopted in different areas. As the use of LLMs continues to grow, many efforts ha…
A Framework for SLO, Carbon, and Wastewater-Aware Sustainable FaaS Cloud Platform Management
Sirui Qi, Hayden Moore, Ninad Hogade +3
Function-as-a-Service (FaaS) is a growing cloud computing paradigm that is expected to reduce the user cost of service over traditional serverful approaches. However, the environme…
CASA: A Framework for SLO and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing
S. Qi, H. Moore, N. Hogade +3
Serverless computing is an emerging cloud computing paradigm that can reduce costs for cloud providers and their customers. However, serverless cloud platforms have stringent perfo…
GreenFaaS: Maximizing Energy Efficiency of HPC Workloads with FaaS
Alok Kamatar, Valerie Hayot-Sasson, Yadu Babuji +6
Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints.…