SPARSE ENCODING AND DECODING AT MIXTURE-OF-EXPERTS LAYER

The computing system described in the abstract includes a Mixture-of-Experts (MoE) layer executed by processing devices to process input tokens and generate expert output tensors.

The processing devices execute the MoE layer by receiving an input tensor with input tokens.
The MoE layer computes a gating function output vector based on the input tensor.
A sparse encoding of the input tensor and gating function output vector is computed to indicate destination expert sub-models.
The input tensor is dispatched to the destination expert sub-models for processing.
An expert output tensor is computed based on the processing at the destination expert sub-models.
The MoE layer output is generated by computing a sparse decoding of the expert output tensor and conveying it to an additional computing process.

Potential Applications

This technology could be applied in natural language processing, image recognition, and other machine learning tasks that require complex decision-making processes.

Problems Solved

This technology solves the problem of efficiently utilizing multiple expert sub-models to process input data and generate accurate outputs in a computing system.

Benefits

The benefits of this technology include improved accuracy in complex decision-making tasks, efficient allocation of resources among expert sub-models, and scalability in handling large amounts of data.

Potential Commercial Applications

A potential commercial application of this technology could be in developing advanced AI systems for industries such as healthcare, finance, and autonomous vehicles.

Possible Prior Art

One possible prior art for this technology could be the use of ensemble learning methods in machine learning, where multiple models are combined to improve prediction accuracy.

Unanswered Questions

How does this technology compare to existing MoE implementations in terms of computational efficiency and accuracy?

This article does not provide a direct comparison with existing MoE implementations in terms of computational efficiency and accuracy. Further research or experimentation would be needed to address this question.

What are the potential limitations or challenges in implementing this technology in real-world applications?

The article does not discuss potential limitations or challenges in implementing this technology in real-world applications. Factors such as data complexity, model training time, and hardware requirements could be important considerations to explore.

Original Abstract Submitted

A computing system including a plurality of processing devices configured to execute a Mixture-of-Experts (MoE) layer. The processing devices are configured to execute the MoE layer at least in part by receiving an input tensor including input tokens. Executing the MoE layer further includes computing a gating function output vector based on the input tensor and computing a sparse encoding of the input tensor and the gating function output vector. The sparse encoding indicates one or more destination expert sub-models. Executing the MoE layer further includes dispatching the input tensor for processing at the one or more destination expert sub-models, and further includes computing an expert output tensor. Executing the MoE layer further includes computing an MoE layer output at least in part by computing a sparse decoding of the expert output tensor. Executing the MoE layer further includes conveying the MoE layer output to an additional computing process.

18318436. SPARSE ENCODING AND DECODING AT MIXTURE-OF-EXPERTS LAYER simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

Contents

SPARSE ENCODING AND DECODING AT MIXTURE-OF-EXPERTS LAYER

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