Optical Switches for Neural Network Data Parallelism
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Solution Overview
Problem
The high computational demands and extensive data communication requirements of large neural networks become prohibitively expensive and energy-intensive due to the large number of connections, making it challenging to efficiently process them across many processors in distributed computing systems.
Innovation Solution
Implementing a distributed processing architecture that utilizes optical network switches to configure sequences of processors across multiple processor groups, enabling forms of parallelism such as data, pipeline, and layer parallelism through Hamiltonian cycles, which optimize communication patterns and reduce congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computations for large neural networks are partitioned across many processors, then processing capability is improved, but communication cost and energy consumption increase prohibitively
Solution Approach 1:
The patent replaces electrical network switches with optical network switches to transmit data between processors. Optical transmission substitutes electrical signals with light-based communication, reducing energy consumption and enabling higher bandwidth communication while partitioning neural network computations across many processors.
Solution Approach 2:
The patent divides the neural network computation into multiple partitions that can be distributed across different processor groups. Each processor group handles a subset of computations, and the optical network switches efficiently coordinate data exchange between these segmented processing units, maintaining high productivity while managing communication overhead.
2Adaptability or versatility
If full bandwidth for arbitrary communication patterns is provided, then communication flexibility is improved, but system cost and energy consumption become prohibitively expensive
Solution Approach 1:
The patent employs dynamically reconfigurable optical network switches that can adapt their connection patterns based on the specific communication requirements of the neural network computation. This dynamic reconfiguration allows the system to provide full bandwidth when needed while avoiding the cost of permanently provisioning all possible communication paths, thus maintaining flexibility without prohibitively increasing system complexity.
Solution Approach 2:
The optical network switches are designed to handle multiple communication patterns and data types through a single unified infrastructure. Rather than providing dedicated full bandwidth paths for every possible communication pattern, the universal optical switching fabric can be dynamically allocated to serve different computation patterns, reducing overall system cost while maintaining adaptability.
Data Source
AI summary
Embodiments of the present disclosure include techniques for processing neural networks. Various forms of parallelism may be implemented using topology that combines sequences of processors. In one embodiment, the present disclosure includes a computer system comprising one or more processor groups, the processor groups each comprising a plurality of processors. A plurality of network switches are coupled to subsets of the plurality of processor groups. In one embodiment, the switches may be optical network switches. Processors in the processor groups may be configurable to form sequences, and the network switches are configurable to form at least one sequence across one or more of the plurality of processor groups to perform neural network computations. Various alternative configurations for creating Hamiltonian cycles are disclosed to support data parallelism, pipeline parallelism, layer parallelism, or combinations thereof.