6 papers
Idiosyncrasies of Programmable Caching Engines
José Peixoto, Alexis Gonzalez, Janki Bhimani +4
Programmable caching engines like CacheLib are widely used in production systems to support diverse workloads in multi-tenant environments. CacheLib's design focuses on performance…
Aurora: Neuro-Symbolic AI Driven Advising Agent
Lorena Amanda Quincoso Lugones, Christopher Kverne, Nityam Sharadkumar Bhimani +4
Academic advising in higher education is under severe strain, with advisor-to-student ratios commonly exceeding 300:1. These structural bottlenecks limit timely access to guidance,…
WSBD: Freezing-Based Optimizer for Quantum Neural Networks
Christopher Kverne, Mayur Akewar, Yuqian Huo +2
The training of Quantum Neural Networks (QNNs) is hindered by the high computational cost of gradient estimation and the barren plateau problem, where optimization landscapes becom…
KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis
Mayur Akewar, Sandeep Madireddy, Dongsheng Luo +1
Solid State Drives (SSDs) are critical to datacenters, consumer platforms, and mission-critical systems. Yet diagnosing their performance and reliability is difficult because data…
Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?
Yuqian Huo, Jinbiao Wei, Christopher Kverne +3
Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The c…
Preparation Meets Opportunity: Enhancing Data Preprocessing for ML Training With Seneca
Omkar Desai, Ziyang Jiao, Shuyi Pei +2
Input data preprocessing is a common bottleneck when concurrently training multimedia machine learning (ML) models in modern systems. To alleviate these bottlenecks and reduce the…