Hardware-Optimized CNN Architecture for GPU Parallel Processing
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Solution Overview
Problem
Current machine learning processing technologies face inefficiencies in training and deploying deep neural networks, particularly in parallel implementations, due to limitations in hardware optimization and data processing capabilities, especially when dealing with large datasets and complex computations.
Innovation Solution
The development of a hardware-optimized convolutional neural network architecture that leverages general-purpose graphics processing units (GPUs) with SIMT architectures, enabling efficient parallel processing and integration with host processors for accelerated machine learning operations, including training and inference tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel processing architectures are used for machine learning, then processing speed and efficiency are improved, but hardware complexity and computational overhead increase
Solution Approach 1:
The patent divides the neural network computation into discrete kernels that can be independently optimized and executed in parallel on GPU architectures. Each kernel represents a segment of the overall computation that can be processed simultaneously, improving productivity while managing hardware complexity through modular organization.
Solution Approach 2:
The patent optimizes computational parameters such as data precision (e.g., using FP16 instead of FP32), batch sizes, and kernel dimensions to maximize parallel processing efficiency. By changing these parameters, the system achieves higher processing speed while reducing the effective computational overhead on the hardware.
2Measurement precision
If larger datasets are processed, then model accuracy and capability are improved, but training time and computational resources increase
Solution Approach 1:
The patent implements data preprocessing and feature extraction steps before the main training process, preparing datasets in advance to reduce the actual training time. This preliminary action allows larger datasets to be processed more efficiently during the training phase, maintaining model accuracy while reducing overall training time.
Solution Approach 2:
The patent employs continuous training approaches where the model is trained on larger datasets through optimized data loading and processing pipelines that minimize idle time. The computational resources remain continuously engaged in useful work, processing larger datasets without proportionally increasing total training time.
3Productivity
If hardware optimization is applied, then processing efficiency is improved, but adaptability to different architectures decreases
Solution Approach 1:
The patent develops a unified framework that can be adapted to multiple GPU architectures (e.g., NVIDIA, AMD, Intel) by implementing architecture-agnostic kernel optimizations. The same optimization principles and techniques can be applied across different hardware platforms, maintaining processing efficiency while preserving adaptability to various architectures.
Data Source
AI summary
In an example, an apparatus comprises at least one execution platform; and logic, at least partially including hardware logic, to receive a trained neural network model in a model optimizer and convert the trained neural network model to an optimized model comprising parameters that are fit to the at least one execution platform. Other embodiments are also disclosed and claimed.