Dynamic Precision GPU Compute for Neural Network Efficiency
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Solution Overview
Problem
Existing machine learning algorithms, particularly neural networks, face inefficiencies in parallel processing due to the limitations of single instruction, multiple thread (SIMT) architectures in general-purpose graphics processing units (GPGPUs), which hinder effective utilization of high-capacity networks and larger datasets.
Innovation Solution
Implementing a dynamic precision approach for neural network compute operations using GPGPUs, where the GPU allocates work through sequences of commands and utilizes dedicated circuitry to efficiently process these commands, optimizing parallel processing across clusters and memory units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic precision adjustment is implemented in neural network compute operations, then processing efficiency and performance are improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic precision adjustment by allowing the system to change data precision levels (e.g., from 32-bit floating point to 16-bit or lower) based on the computational requirements of different neural network operations. This enables the processing system to adaptively optimize performance for each operation type, improving overall productivity while managing device complexity through targeted precision changes rather than system-wide complexity increases
Solution Approach 2:
The invention changes the precision parameter of computational operations dynamically during neural network processing. By adjusting precision levels according to the specific needs of different layers and operations in the neural network, the system achieves higher processing efficiency without requiring fundamentally more complex device architecture, thereby resolving the contradiction between productivity improvement and device complexity
2Measurement precision
If higher precision is used in neural network computations, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent applies different precision levels to different parts of the neural network computation based on local requirements. Critical computations that demand high accuracy use higher precision, while less sensitive operations use lower precision. This localized quality approach maintains necessary measurement precision where required while significantly reducing overall energy consumption across the entire computational system
Solution Approach 2:
The system dynamically changes the precision parameter during computation based on the specific operational context. By transitioning between different precision modes (e.g., high precision for training, lower precision for inference), the patent optimizes the balance between measurement precision and energy consumption, avoiding the constant high energy cost of maintaining maximum precision throughout all operations
3Device complexity
If fixed precision is used throughout the system, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent introduces dynamic precision selection that allows the system to adapt to different computational requirements without fundamentally increasing device complexity. The system can switch between precision modes based on operational context, maintaining high adaptability while keeping the underlying hardware architecture relatively simple and manageable
Solution Approach 2:
The invention creates a universal processing framework that can handle multiple precision requirements using the same hardware infrastructure. By designing the system to be multi-functional in terms of precision handling, it achieves high adaptability across different neural network operations without proportionally increasing device complexity, as the same hardware can serve multiple precision needs
Data Source
AI summary
In an example, an apparatus comprises a compute engine comprising a high precision component and a low precision component; and logic, at least partially including hardware logic, to receive instructions in the compute engine; select at least one of the high precision component or the low precision component to execute the instructions; and apply a gate to at least one of the high precision component or the low precision component to execute the instructions. Other embodiments are also disclosed and claimed.