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Found 8 papers, 1 papers with code
Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier
We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models.
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.
Pathways: Asynchronous Distributed Dataflow for ML
We present the design of a new large scale orchestration layer for accelerators.
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.
Equality Saturation for Tensor Graph Superoptimization
However, we observe that existing approaches for tensor graph superoptimization both in production and research frameworks apply substitutions in a sequential manner.
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.
GDP: Generalized Device Placement for Dataflow Graphs
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices.
