Neural Network Multiplexing Layer for High-Throughput Processing
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Solution Overview
Problem
Deep learning models, such as OpenAI's GPT-3, require significant computational resources and energy, leading to high CO2 emissions and operational burdens as they are scaled up and democratized.
Innovation Solution
The method involves a multiplexing phase where multiple inputs are transformed and combined into a single compact representation, which is then processed by a base neural network. A demultiplexing phase follows, where the output is converted back into independent representations for prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large neural network models are used to improve effectiveness, then model performance is improved, but computational resource consumption and energy usage increase significantly
Solution Approach 1:
The system segments the processing of multiple input sequences into a single combined representation that is processed by one neural network instance. The multiplexing layer divides and reorganizes multiple input sequences into a unified structure, allowing a single model to handle multiple inputs simultaneously, thereby reducing the total computational resources needed compared to running separate models for each input.
Solution Approach 2:
A single neural network model is designed to perform multiple functions by processing multiple input sequences simultaneously through the multiplexing mechanism. The model universalizes its capability to handle diverse inputs through the multiplexing layer, eliminating the need for separate specialized models for each input sequence and reducing overall energy consumption.
2Productivity
If multiple input sequences are processed simultaneously to improve throughput, then processing speed is improved, but the complexity of the neural network architecture increases
Solution Approach 1:
The multiplexing layer serves as an intermediary component between the input sequences and the neural network. It mediates the interaction by transforming multiple input sequences into a unified representation that the neural network can process efficiently. This intermediary layer handles the complexity of managing multiple inputs without adding complexity to the core neural network architecture itself.
Solution Approach 2:
The system adds a new dimensional aspect to the input data structure through the multiplexing layer, which organizes multiple input sequences into a multi-dimensional representation. This dimensional transformation allows the neural network to process multiple inputs simultaneously by leveraging the additional structure rather than increasing the network's inherent complexity.
Data Source
AI summary
Disclosed is a technique for improving the throughput of a neural network, using multiplexing and demultiplexing of information. Specifically, the multiplexing may include receiving a plurality of inputs, generating transformed inputs by performing, via a multiplexing layer, a transformation to each input of the plurality of inputs, and combining the transformed inputs into a single compact representation of the plurality of inputs. The demultiplexing may include receiving an output from a neural network, generating a plurality of values by converting, via a demultiplexing layer, the output back into independent representations, and producing predictions for each input based on the plurality of values. Further improvements may be seen when pretraining of the neural network and/or high-throughput transformers are incorporated.