Distributed Machine Learning Training with Hardware Acceleration
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Solution Overview
Problem
Current machine learning processing technologies face inefficiencies in training and deploying deep neural networks, particularly in handling large datasets and dynamic environments, due to high computational requirements and resource intensive operations such as background segmentation and feature detection.
Innovation Solution
The integration of a hardware engine that accelerates weight updates and the use of a bottom-up, modular construction approach for deep neural networks, enabling hardware acceleration of weight averaging and voting operations, and prioritization of specific layers for efficient deployment in various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional machine learning processing is used for training deep neural networks on large datasets, then the models can be trained with comprehensive data, but the computational load and resource requirements become excessively high
Solution Approach 1:
The patent divides the training process into multiple distributed worker nodes that process different portions of the dataset in parallel. Each worker node handles a segment of the computational workload, allowing the system to process large datasets without concentrating excessive computational load on a single processor.
Solution Approach 2:
The patent transitions from single-node sequential processing to multi-node distributed processing across a network. This adds a spatial dimension to the computational architecture, enabling parallel processing of training data across multiple machines to reduce overall computational burden per node.
2Quantity of substance
If traditional machine learning processing is used for training deep neural networks on large datasets, then the models can be trained with comprehensive data, but the training time increases significantly
Solution Approach 1:
The training process is segmented into parallel tasks distributed across multiple worker nodes. Each node simultaneously processes different batches or portions of the training data, reducing the overall training time compared to sequential processing on a single machine.
Solution Approach 2:
The patent implements continuous training pipelines where worker nodes continuously process training data batches without idle time. Data loading, preprocessing, and model training operations are overlapped and executed continuously across the distributed system, maximizing resource utilization and reducing total training time.
3Use of energy by moving object
If hardware acceleration is implemented for weight updates, then the computational load is reduced, but the device complexity increases
Solution Approach 1:
The patent introduces a parameter server as an intermediary component that manages weight updates and coordinates communication between worker nodes. This mediator handles the complexity of distributed synchronization and aggregation, allowing individual workers to focus on computation while the parameter server manages the coordination overhead.
Solution Approach 2:
The parameter server performs multiple functions including weight storage, gradient aggregation, and synchronization coordination. This multi-functional component consolidates various management tasks into a single system, reducing overall system complexity despite adding hardware acceleration capabilities.
Data Source
AI summary
In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.