12 citations · 12 across the 10 of their papers we have counts for
10 papers
IRIS: A Compiler for Distributed Quantum Systems
Won Joon Yun, Dhilan Nag, Sneha Ballabh +3
Distributed Quantum Computers (DQCs) enable large system sizes by connecting smaller chips via photonic interconnects. DQCs use teleportation to relocate qubits and execute CNOTs b…
GEM: GPU-Variability-Aware Expert to GPU Mapping for MoE Systems
Sourish Wawdhane, Avinash Kumar, Poulami Das
Mixture-of-Expert (MoE) models enable efficient inference by employing smaller experts and activating only a subset of them per token. MoE serving engines distribute experts across…
Test-Time Speculation
Avinash Kumar, Sujay Sanghavi, Poulami Das
Speculative decoding accelerates LLM inference by using a fast draft model to generate tokens and a more accurate target model to verify them. Its performance depends on the $\text…
Are LLMs Good For Quantum Software, Architecture, and System Design?
Sourish Wawdhane, Poulami Das
Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc. However, despite decades…
HiSpec: Hierarchical Speculative Decoding for LLMs
Avinash Kumar, Sujay Sanghavi, Poulami Das
Speculative decoding accelerates LLM inference by using a smaller draft model to speculate tokens that a larger target model verifies. Verification is often the bottleneck (e.g. ve…
Context Switching for Secure Multi-programming of Near-Term Quantum Computers
Avinash Kumar, Meng Wang, Chenxu Liu +3
Multi-programming quantum computers improve device utilization and throughput. However, crosstalk from concurrent two-qubit CNOT gates poses security risks, compromising the fideli…
HELIOS: Adaptive Model And Early-Exit Selection for Efficient LLM Inference Serving
Avinash Kumar, Shashank Nag, Jason Clemons +2
Early-Exit Large Language Models (EE-LLMs) enable high throughput inference by allowing tokens to exit early at intermediate layers. However, their throughput is limited by the com…
Dialogue Without Limits: Constant-Sized KV Caches for Extended Responses in LLMs
Ravi Ghadia, Avinash Kumar, Gaurav Jain +2
Autoregressive Transformers rely on Key-Value (KV) caching to accelerate inference. However, the linear growth of the KV cache with context length leads to excessive memory consump…
Transpiler-Architecture Co-Design to Curb Clifford Costs in Fault-Tolerant Quantum Computing
Meng Wang, Chenxu Liu, Samuel Stein +4
Quantum Error Correction (QEC) codes form the foundation of Fault-Tolerant Quantum Computing (FTQC) and predominantly use the Clifford+T gate set. Recently, Clifford operations hav…
Qoncord: A Multi-Device Job Scheduling Framework for Variational Quantum Algorithms
Meng Wang, Poulami Das, Prashant J. Nair
Quantum computers face challenges due to limited resources, particularly in cloud environments. Despite these obstacles, Variational Quantum Algorithms (VQAs) are considered promis…