: In recent years, requests for automotive seat comfort are increasing. An important issue of them is long-term seat comfort. Until now, the study for long-term seat comfort has been studied mainly using driver's questionnaire, changing adrenalin and electromyography. Actually the results and methodologies of them are difficult to apply to seat development and design because of money and time required. In this study, we developed Seating Feel Curve for seat comfort evaluation and a long-term seat comfort evaluation which can be applied to the development of seat comfort using seat support.
With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile devices and TVs. Existing PTQ schemes, however, consume considerable time and resources, which could be a bottleneck in real situations where frequent model updates and multiple hyper-parameter tunings are required. As a costeffective alternative, one-shot PTQ schemes have been proposed. Still, the performance is somewhat limited because they cannot consider the inter-layer dependency within the attention module, which is a very important feature of Transformers. In this paper, we thus propose a novel PTQ algorithm that balances accuracy and efficiency. The key idea of the proposed algorithm called aespa is to perform quantization layer-wise for efficiency while considering cross-layer dependency to preserve the attention score. Through extensive experiments on various language models and complexity analysis, we demonstrate that aespa is accurate and efficient in quantizing Transformer models.
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