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Found 5 papers, 2 papers with code
Emerging Platforms Meet Emerging LLMs: A Year-Long Journey of Top-Down Development
While a traditional bottom-up development pipeline fails to close the gap timely, we introduce TapML, a top-down approach and tooling designed to streamline the deployment of ML systems on diverse platforms, optimized for developer productivity.
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models.
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
We propose SparseTIR, a sparse tensor compilation abstraction that offers composable formats and composable transformations for deep learning workloads.
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives.
Tensor Program Optimization with Probabilistic Programs
Experimental results show that MetaSchedule can cover the search space used in the state-of-the-art tensor program optimization frameworks in a modular way.
