4 citations · 6 across the 5 of their papers we have counts for
5 papers
Streaming Tensor Programs: A Streaming Abstraction for Dynamic Parallelism
Gina Sohn, Genghan Zhang, Konstantin Hossfeld +5
Dynamic behaviors are becoming prevalent in tensor applications, like machine learning, where many widely used models contain data-dependent tensor shapes and control flow. However…
FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow
Rubens Lacouture, Nathan Zhang, Ritvik Sharma +4
As deep learning models scale, sparse computation and specialized dataflow hardware have emerged as powerful solutions to address efficiency. We propose FuseFlow, a compiler that c…
DFModel: Design Space Optimization of Large-Scale Systems Exploiting Dataflow Mappings
Sho Ko, Nathan Zhang, Olivia Hsu +2
We propose DFModel, a modeling framework for mapping dataflow computation graphs onto large-scale systems. Mapping a workload to a system requires optimizing dataflow mappings at v…
Implementing and Optimizing the Scaled Dot-Product Attention on Streaming Dataflow
Gina Sohn, Nathan Zhang, Kunle Olukotun
Transformer models serve as the backbone of many state-ofthe-art language models, and most use the scaled dot-product attention (SDPA) mechanism to capture relationships between to…
On the Steganographic Capacity of Selected Learning Models
Rishit Agrawal, Kelvin Jou, Tanush Obili +6
Machine learning and deep learning models are potential vectors for various attack scenarios. For example, previous research has shown that malware can be hidden in deep learning m…