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Found 9 papers, 4 papers with code
Accelerating Retrieval-Augmented Language Model Serving with Speculation
Retrieval-augmented language models (RaLM) have demonstrated the potential to solve knowledge-intensive natural language processing (NLP) tasks by combining a non-parametric knowledge base with a parametric language model.
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs.
Learning Large Graph Property Prediction via Graph Segment Training
Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint.
Optimizing Memory Mapping Using Deep Reinforcement Learning
We also introduce a Reinforcement Learning agent, mallocMuZero, and show that it is capable of playing this game to discover new and improved memory mapping solutions that lead to faster execution times on real ML workloads on ML accelerators.
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
In this paper, we introduce GRANITE, a new machine learning model that estimates the throughput of basic blocks across different microarchitectures.
Neural Architecture Search using Property Guided Synthesis
To enable this approach, we also propose a novel efficient synthesis procedure, which accepts a set of promising program properties, and returns a satisfying neural architecture.
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Partitioning ML graphs for MCMs is particularly hard as the search space grows exponentially with the number of chiplets available and the number of nodes in the neural network.
Transferable Graph Optimizers for ML Compilers
Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code.
A Learned Performance Model for Tensor Processing Units
Accurate hardware performance models are critical to efficient code generation.
