6 citations · 11 across the 6 of their papers we have counts for
6 papers
Proteus: Preserving Model Confidentiality during Graph Optimizations
Yubo Gao, Maryam Haghifam, Christina Giannoula +3
Deep learning (DL) models have revolutionized numerous domains, yet optimizing them for computational efficiency remains a challenging endeavor. Development of new DL models typica…
TorchProbe: Fuzzing Dynamic Deep Learning Compilers
Qidong Su, Chuqin Geng, Gennady Pekhimenko +1
Static and dynamic computational graphs represent two distinct approaches to constructing deep learning frameworks. The former prioritizes compiler-based optimizations, while the l…
The Synergy of Speculative Decoding and Batching in Serving Large Language Models
Qidong Su, Christina Giannoula, Gennady Pekhimenko
Large Language Models (LLMs) like GPT are state-of-the-art text generation models that provide significant assistance in daily routines. However, LLM execution is inherently sequen…
TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization
Anand Jayarajan, Wei Zhao, Yudi Sun +1
Stream processing engines (SPEs) are widely used for large scale streaming analytics over unbounded time-ordered data streams. Modern day streaming analytics applications exhibit d…
Optimizing Data Collection in Deep Reinforcement Learning
James Gleeson, Daniel Snider, Yvonne Yang +3
Reinforcement learning (RL) workloads take a notoriously long time to train due to the large number of samples collected at run-time from simulators. Unfortunately, cluster scale-u…
Practical Data Compression for Modern Memory Hierarchies
Gennady Pekhimenko
In this thesis, we describe a new, practical approach to integrating hardware-based data compression within the memory hierarchy, including on-chip caches, main memory, and both on…