Memory-Augmented Neural Networks on FPGA for Real-Time and Energy-Efficient Question Answering
| Autor: | Jaehee Jang, Byunggook Na, Seijoon Kim, Sungroh Yoon, Seongsik Park |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science Inference 02 engineering and technology Energy consumption 020202 computer hardware & architecture Memory management Computer engineering Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Question answering Electrical and Electronic Engineering Field-programmable gate array Software Efficient energy use |
| Zdroj: | IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 29:162-175 |
| ISSN: | 1557-9999 1063-8210 |
| Popis: | Memory-augmented neural networks (MANNs) were introduced to handle long-term dependent data efficiently. MANNs have shown promising results in question answering (QA) tasks that require holding contexts for answering a given question. As demands for QA on edge devices have increased, the utilization of MANNs in resource-constrained environments has become important. To achieve fast and energy-efficient inference of MANNs, we can exploit application-specific hardware accelerators on field-programmable gate arrays (FPGAs). Although several accelerators for conventional deep neural networks have been designed, it is difficult to efficiently utilize the accelerators with MANNs due to different requirements. In addition, characteristics of QA tasks should be considered for further improving the efficiency of inference on the accelerators. To address the aforementioned issues, we propose an inference accelerator of MANNs on FPGA. To fully utilize the proposed accelerator, we introduce fast inference methods considering the features of QA tasks. To evaluate our proposed approach, we implemented the proposed architecture on an FPGA and measured the execution time and energy consumption for the bAbI data set. According to our thorough experiments, the proposed methods improved speed and energy efficiency of the inference of MANNs up to about 25.6 and 28.4 times, respectively, compared with those of CPU. |
| Databáze: | OpenAIRE |
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