Parallel Neural Network Architecture for NLP Inference Latency

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Solution Overview

Problem

Sequence-to-sequence (Seq2Seq) AI models face inefficiencies when implemented on hardware processing devices due to temporal dependencies, leading to bottlenecks in processing and inference generation, as output from one time step needs to be sent back to the host CPU and reprocessed, creating an inefficient step-by-step processing approach.

Innovation Solution

A neural network architecture that replaces Seq2Seq models with a parallel processing model using a speech-to-text encoder and a Bifrost speech recognizable engine, eliminating the need for step-by-step processing by generating multiple parallel streams of computation, each performing equivalent operations, allowing data to flow continuously without the need for back-and-forth data transfer.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If Seq2Seq model architecture is used, then natural language processing tasks can be performed, but processing efficiency deteriorates due to step-by-step execution requiring repeated data transfer between hardware accelerator and host CPU

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidinference time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The model processing is segmented into distinct functional components (encoder, parallel processing units, decoder) that can operate independently and simultaneously, eliminating the sequential bottleneck where the entire model must wait for each time step to complete before proceeding to the next

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The parallel processing architecture enables continuous data flow through the system, where multiple operations occur simultaneously rather than sequentially, maintaining continuous useful action without interruptions for data transfer and reprocessing

Inventive Principle:

Principle #20Continuity of useful action

2Productivity

If Seq2Seq model with temporal dependencies is implemented on hardware accelerator, then sequence processing capability is achieved, but device utilization deteriorates due to frequent data transfer to host CPU

Engineering Contradiction:

ImprovethroughputVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

Multiple processing functions that were previously distributed across sequential time steps are merged into a single parallel execution stage, allowing the hardware accelerator to process multiple sequence elements simultaneously without requiring intermediate transfers to the host CPU

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent introduces intermediate processing structures (parallel processing units with shared memory) that act as mediators between the encoder and decoder, enabling efficient data sharing and processing without requiring frequent transfers to the host CPU

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20240249716A1Model substitution for efficient development of a solution

Publication Date: 2024.07.25 GROQ INC

AI summary

A neural network architecture for natural language processing is provided. The neural network architecture comprises: a speech-to-text encoder configured to encode an input speech signal; a Bifrost speech recognizable engine configured for processing the encoded speech signal to generate a speech recognized signal corresponding to the input speech signal; and a decoder configured to decode the speech recognized signal and to generate the output sequence.