7 papers
CODA: How to Mitigate ColumnDisturb for (Almost) Free?
Moinuddin Qureshi
The paper introduces CODA, a set of techniques that dramatically cut down the extra activation operations needed to protect DRAM subarrays from the newly identified ColumnDisturb e…
SiFAR: Synchronization-Free All-Reduce for Low-Latency LLM Inference
Hritvik Taneja, Anish Saxena, Abhishek Revinipati +3
The rise of reasoning models and agentic systems has made LLM token-generation latency a key bottleneck. Unlike chatbots, whose latency gains saturate at human reading speed, these…
TileLens: Efficiently Using Large-Granularity Memory Systems with Transparent Two-Dimensional Memory Layout
Jae Hyung Ju, Euijun Chung, Hritvik Taneja +4
Large Language Model (LLM) inference is bottlenecked by the capacity and bandwidth of GPU High-Bandwidth Memory (HBM). Recent proposals, such as High-Bandwidth Flash (HBF) and RoMe…
The Avatar Cache: Enabling On-Demand Security with Morphable Cache Architecture
Anubhav Bhatla, Navneet Navneet, Moinuddin Qureshi +1
The sharing of the last-level cache (LLC) among multiple cores makes it vulnerable to cross-core conflict- and occupancy-based attacks. Despite extensive prior work, modern process…
Computer Science Challenges in Quantum Computing: Early Fault-Tolerance and Beyond
Jens Palsberg, Jason Cong, Yufei Ding +7
Quantum computing is entering a period in which progress will be shaped as much by advances in computer science as by improvements in hardware. The central thesis of this report is…
Utility-Driven Speculative Decoding for Mixture-of-Experts
Anish Saxena, Po-An Tsai, Hritvik Taneja +2
GPU memory bandwidth is the main bottleneck for low-latency Large Language Model (LLM) inference. Speculative decoding leverages idle GPU compute by using a lightweight drafter to…