Parallel GPU Translation Encoding and Decoding for Speed
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Solution Overview
Problem
Current translation technologies are inefficient due to the time and complexity involved in delivering accurate translations across different languages, often requiring multiple stages and processes, which can lead to inconvenience in communication.
Innovation Solution
A parallel processing-based translation method using multiple graphic processor units (GPUs) to perform multiple translation processes in parallel, where each GPU includes a translation encoder and decoder, and the source sentence is input to multiple GPUs or variations of the sentence are input to different GPUs to generate candidate sentences in a second language, with scoring and rescoring models used to select the final translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple translation processes are performed sequentially to ensure accuracy, then translation quality is improved, but translation time increases significantly
Solution Approach 1:
The translation system is divided into multiple independent translation processes (first translation process, second translation process, etc.), each handling a portion of the translation task. These segmented processes operate in parallel rather than sequentially, allowing multiple candidate translations to be generated simultaneously, thus reducing overall translation time while maintaining accuracy through comparison and selection of the best candidate.
Solution Approach 2:
The patent introduces a parallel processing dimension by executing multiple translation processes simultaneously across different processors or computing units. This dimensional change from sequential to parallel execution enables the system to generate multiple candidate translations at the same time, resolving the contradiction between translation accuracy and translation time.
2Productivity
If multiple translation processes are performed in parallel to reduce time, then translation speed is improved, but system complexity increases
Solution Approach 1:
The patent employs multiple encoders and decoders that can be configured to perform different translation processes. These components serve universal functions by handling various language pairs and translation tasks, reducing the need for specialized hardware for each translation process and thereby managing system complexity while enabling parallel processing.
Solution Approach 2:
The system creates multiple copies of the translation processing components (encoders and decoders) to run parallel translation processes. Instead of designing a single complex sequential processor, the patent uses simpler replicated units that operate simultaneously, reducing individual component complexity while achieving high overall translation speed through parallel execution.
3Measurement precision
If multiple encoders and decoders are used to generate candidate sentences, then translation accuracy is improved, but computational resources increase
Solution Approach 1:
The patent applies preliminary filtering and selection mechanisms where candidate sentences are generated by multiple encoders and decoders, then evaluated and ranked using scoring functions. This preliminary action of generating multiple candidates followed by systematic evaluation allows the system to invest computational resources efficiently, focusing on generating and selecting the best candidates rather than optimizing every possible translation path.
Solution Approach 2:
The system varies parameters such as the number of candidate sentences generated, the complexity of encoding/decoding models, and the selection criteria to balance translation accuracy with computational resource consumption. By adjusting these parameters, the patent enables flexible resource allocation that maintains high translation accuracy while adapting to available computational resources.
Data Source
AI summary
A translation method and apparatus may respectively perform or include: using one or more processors, plural different translation processes, in parallel, for a source sentence in a first language, including encoding, to generate respective feature vectors, the source sentence in each of two or more translation processes of the plural translation processes or the source sentence and a variation of the source sentence in respective translation processes of the plural translation processes, and decoding each of the respective feature vectors to generate respective plural candidate sentences in a second language; and selecting a final sentence in the second language from the respective plural candidate sentences in the second language.