Tensor Tile Compilation for Neural Network Memory Constraints
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Solution Overview
Problem
Deep neural networks (DNNs) require significant computational resources for training and inference, necessitating techniques to reduce computational costs and size without compromising accuracy.
Innovation Solution
Compiling tensor operators for neural network models based on tensor tile configurations, which involves determining optimal tensor tile shapes for memory architectures of hardware devices, generating code for execution, and optimizing memory performance through padding and data reuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are increased in size to achieve better accuracies, then model accuracy is improved, but computational cost increases
Solution Approach 1:
The patent applies segmentation by dividing large neural network computations into smaller tensor tile operations that can be processed in parallel across multiple hardware cores. The tensor expressions are broken down into manageable tiles that fit within memory constraints, enabling efficient processing of large models without proportionally increasing computational cost.
Solution Approach 2:
The patent introduces a spatial dimension optimization by organizing tensor computations into multi-dimensional tile structures that optimize memory access patterns. By transforming the computation layout from traditional sequential processing to multi-dimensional parallel tile processing, the system achieves better hardware utilization and reduced computational overhead.
2Measurement precision
If deep neural networks are increased in size to achieve better accuracies, then model accuracy is improved, but model size increases
Solution Approach 1:
The patent segments large tensor operations into smaller tile-based units that can be processed and stored more efficiently. This segmentation allows the model to maintain high accuracy while reducing the actual memory footprint by processing data in manageable chunks rather than requiring all data to be simultaneously available in memory.
3Productivity
If tensor tile configurations are optimized for memory architectures, then memory performance is improved, but code complexity increases
Solution Approach 1:
The patent creates a universal tensor tile configuration system that can adapt to different hardware architectures through a unified interface. The same tile-based compilation approach works across various memory architectures, reducing the need for architecture-specific code while maintaining optimized memory performance through automatic configuration selection.
Data Source
AI summary
Embodiments of the present disclosure include systems and methods for compiling tensor operators for neural network models based on tensor tile configurations. A tensor expression for a neural network model is received. A tensor tile configuration for the tensor expression is determined based on specifications associated with a set of hardware devices. Based on the tensor tile configuration, a set of code for implementing the tensor expression for the neural network model is generated. The set of code is for execution on the set of hardware.