MGen: A Framework for Energy-Efficient In-ReRAM Acceleration of Multi-Task BERT

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Recently, multiple transformer models, such as BERT, have been utilized together to support multiple natural language processing (NLP) tasks in a system, also known as multi-task BERT. Multi-task BERT with very high weight parameters increases the area requirement of a processing in resistive memory (ReRAM) architecture, and several works have attempted to address this model size issue. Despite the reduced parameters, the number of multi-task BERT computations remains the same, leading to massive energy consumption in ReRAM-based deep neural network (DNN) accelerators. Therefore, we suggest a framework for better energy efficiency during the ReRAM acceleration of multi-task BERT. First, we analyze the inherent redundancies of multi-task BERT and the computational properties of the ReRAM-based DNN accelerator, after which we propose what is termed the model generator, which produces optimal BERT models supporting multiple tasks. The model generator reduces multi-task BERT computations while maintaining the algorithmic performance. Furthermore, we present task scheduler, which adjusts the execution order of multiple tasks, to run the produced models efficiently. As a result, the proposed framework achieves maximally 4.4x higher energy efficiency over the baseline, and it can also be combined with the previous multi-task BERT works to achieve both a smaller area and higher energy efficiency.
Publisher
IEEE COMPUTER SOC
Issue Date
2023-11
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON COMPUTERS, v.72, no.11, pp.3140 - 3152

ISSN
0018-9340
DOI
10.1109/TC.2023.3288749
URI
http://hdl.handle.net/10203/314832
Appears in Collection
EE-Journal Papers(저널논문)
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