activity
20182026
most citedPruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration

53 citations · 119 across the 19 of their papers we have counts for

collaborators

20 papers

cs.CR2026

ARM MTE Performance in Practice (Extended Version)

Taehyun Noh, Yingchen Wang, Tal Garfinkel +4

We present the first comprehensive analysis of ARM MTE hardware performance on four different microarchitectures: ARM Big (A7x), Little (A5x), and Performance (Cortex-X) cores on t…

cs.DC2025

VLCs: Managing Parallelism with Virtualized Libraries

Yineng Yan, William Ruys, Hochan Lee +11

As the complexity and scale of modern parallel machines continue to grow, programmers increasingly rely on composition of software libraries to encapsulate and exploit parallelism.…

cs.MA2025

Sherlock: Reliable and Efficient Agentic Workflow Execution

Yeonju Ro, Haoran Qiu, Íñigo Goiri +6

With the increasing adoption of large language models (LLM), agentic workflows, which compose multiple LLM calls with tools, retrieval, and reasoning steps, are increasingly replac…

cs.AR2025

Presto: Hardware Acceleration of Ciphers for Hybrid Homomorphic Encryption

Yeonsoo Jeon, Mattan Erez, Michael Orshansky

Hybrid Homomorphic Encryption (HHE) combines symmetric key and homomorphic encryption to reduce ciphertext expansion crucial in client-server deployments of HE. Special symmetric c…

cs.CR2025

Sudoku: Decomposing DRAM Address Mapping into Component Functions

Minbok Wi, Seungmin Baek, Seonyong Park +2

Decomposing DRAM address mappings into component-level functions is critical for understanding memory behavior and enabling precise RowHammer attacks, yet existing reverse-engineer…

cs.OS2023★ 1 cited

MaxMem: Colocation and Performance for Big Data Applications on Tiered Main Memory Servers

Amanda Raybuck, Wei Zhang, Kayvan Mansoorshahi +3

We present MaxMem, a tiered main memory management system that aims to maximize Big Data application colocation and performance. MaxMem uses an application-agnostic and lightweight…