40 citations · 70 across the 11 of their papers we have counts for
11 papers
TensorRight: Automated Verification of Tensor Graph Rewrites
Jai Arora, Sirui Lu, Devansh Jain +9
Tensor compilers, essential for generating efficient code for deep learning models across various applications, employ tensor graph rewrites as one of the key optimizations. These…
Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput
Arissa Wongpanich, Tayo Oguntebi, Jose Baiocchi Paredes +6
Recent years have seen the emergence of machine learning (ML) workloads deployed in warehouse-scale computing (WSC) settings, also known as ML fleets. As the computational demands…
UQE: A Query Engine for Unstructured Databases
Hanjun Dai, Bethany Yixin Wang, Xingchen Wan +7
Analytics on structured data is a mature field with many successful methods. However, most real world data exists in unstructured form, such as images and conversations. We investi…
Accelerating Retrieval-Augmented Language Model Serving with Speculation
Zhihao Zhang, Alan Zhu, Lijie Yang +4
Retrieval-augmented language models (RaLM) have demonstrated the potential to solve knowledge-intensive natural language processing (NLP) tasks by combining a non-parametric knowle…
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao +4
Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal…
Learning Large Graph Property Prediction via Graph Segment Training
Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija +5
Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during trainin…
Optimizing Memory Mapping Using Deep Reinforcement Learning
Pengming Wang, Mikita Sazanovich, Berkin Ilbeyi +16
Resource scheduling and allocation is a critical component of many high impact systems ranging from congestion control to cloud computing. Finding more optimal solutions to these p…
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
Ondrej Sykora, Phitchaya Mangpo Phothilimthana, Charith Mendis +1
Analytical hardware performance models yield swift estimation of desired hardware performance metrics. However, developing these analytical models for modern processors with sophis…
Neural Architecture Search using Property Guided Synthesis
Charles Jin, Phitchaya Mangpo Phothilimthana, Sudip Roy
In the past few years, neural architecture search (NAS) has become an increasingly important tool within the deep learning community. Despite the many recent successes of NAS, howe…
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon +6
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of machine learning (ML) accelerators while delivering performance and energy efficiency on par with a monolithic l…
A Learned Performance Model for Tensor Processing Units
Samuel J. Kaufman, Phitchaya Mangpo Phothilimthana, Yanqi Zhou +4
Accurate hardware performance models are critical to efficient code generation. They can be used by compilers to make heuristic decisions, by superoptimizers as a minimization obje…