Neural Network Layer Duplication for Pipeline Parallel Inference
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Solution Overview
Problem
The increasing complexity and size of deep learning models, coupled with larger datasets, lead to prolonged training times and memory constraints, particularly in medical image analysis, due to synchronization and communication overheads, with existing parallelism techniques like data and model parallelism failing to effectively handle large models and 3D CNNs.
Innovation Solution
Implementing a modified neural network architecture, such as a parallel U-Net, that duplicates output from one layer to multiple subsequent layers and distributes these layers across separate computing resources, utilizing pipeline parallelism to reduce dependencies and memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are made more complex and larger to handle bigger datasets, then model accuracy and capability are improved, but training time and memory consumption increase significantly
Solution Approach 1:
The neural network is divided into multiple independent segments or layers that can be processed separately. Each segment handles a specific portion of the computation, allowing parallel execution across multiple devices. This segmentation reduces the sequential dependency in training workflows and enables more efficient utilization of distributed computing resources.
Solution Approach 2:
The patent transitions from single-device sequential processing to multi-device parallel processing by adding a spatial dimension to the computation architecture. Multiple computing devices operate simultaneously on different segments of the neural network, effectively distributing the computational load across a network topology rather than relying on a single processor's sequential capabilities.
2Productivity
If more computing devices are employed to handle large datasets and complex models, then processing capability is improved, but synchronization and communication overhead increase
Solution Approach 1:
The neural network computation is segmented into independent layers or blocks that can be assigned to different computing devices. Each device processes its assigned segment autonomously with minimal coordination required, reducing synchronization overhead. The segmentation creates natural boundaries that minimize the need for frequent inter-device communication.
Solution Approach 2:
Data and model parameters are pre-partitioned and distributed to computing devices before training begins. This preliminary distribution establishes clear ownership and processing responsibilities for each device, eliminating the need for complex real-time synchronization during training execution. Devices operate on pre-assigned data chunks with minimal coordination.
3Speed
If conventional parallelism techniques are used to handle large models, then some processing speedup is achieved, but memory constraints and dependency issues persist
Solution Approach 1:
The model and data are segmented such that each computing device holds only the necessary portion in memory, reducing individual device memory requirements. This segmentation allows the system to handle larger overall models than any single device could accommodate alone, effectively overcoming memory constraints through distributed storage and computation.
Solution Approach 2:
The patent introduces intermediary mechanisms for efficient data and gradient exchange between devices. These intermediaries manage the flow of information across the distributed system, reducing communication overhead and enabling scalable expansion to multiple devices without proportionally increasing synchronization complexity.
Data Source
AI summary
Apparatuses, systems, and techniques are described herein to speed up inferencing in a neural network by copying output from one layer of the neural network to another computing resource based on dependencies among layers in the network. In at least one embodiment, a processor comprising one or more circuits causes two or more subsequent layers of one or more neural networks to be performed on separate computing resources from a previous layer of the one or more neural networks.