Jamilan, S. (CSE) – Profile-guided Compiler Optimizations for Data Center Workloads

Engineering 2 Engineering 2 1156 High Street, Santa Cruz
Hybrid Event

Modern applications, such as data center workloads, have become increasingly complex. These applications primarily operate on massive datasets, which involve large memory footprints, irregular access patterns, and complex control and data flows. The processor-memory speed gap, combined with these complexities, can lead to unexpected performance inefficiencies in these applications, preventing them from achieving optimal performance. […]

de Priester, J. (ECE) – Hybrid Reinforcement Learning

Jack Baskin Engineering Baskin Engineering 1156 High Street, Santa Cruz
Hybrid Event

Reinforcement Learning (RL) is a machine learning paradigm that trains a decision maker, or policy, by learning from interaction with an environment. The power of RL lies in its ability to learn complex strategies without explicit human instruction, which can lead to better solutions that human designers overlook in domains ranging from robotics to scientific […]

Ferdous, N. (CSE) – SPECSIM : A Simulation Infrastructure Mitigating Transient Timing Attacks

Engineering 2 Engineering 2 1156 High Street, Santa Cruz
Hybrid Event

   Transient execution attacks are serious security threats in modern-day processors. Out-of-order execution compels the processor to access data that should not be otherwise perceived. Leakage of that secret information creates a covert channel for the attacker for various types of transient and speculative attacks. Transient based execution attacks emanate when the secret information is leaked […]

Wang, Y. (CSE) – Toward Practical and Effective Large Language Model Unlearning

Virtual Event

The growing integration of Large Language Models (LLMs) into real-world applications has heightened concerns about their trustworthiness, as models may reveal private information, reproduce copyrighted content, propagate biases, or generate harmful instructions. These risks, alongside emerging privacy regulations, motivate the need for LLM unlearning, methods that remove the influence of specific data while preserving overall […]