4 accepted papers
Most of the existing quantum neural network models, such as variational quantum circuits (VQCs), are limited in their ability to explore the non-linear relationships in input data. This gradually becomes the main obstacle for it to tackle realistic applications, such as natural language processing,…
Pretrained large language models (LLMs) have excelled in a variety of natural language processing (NLP) tasks, including summarization, question answering, and translation. However, LLMs pose significant security risks due to their tendency to memorize training data, leading to potential privacy bre…
Transformer-based deep learning models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model meets both resource and real-time specifications of mob…
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a \underline R ow-wise \underline M ixed-\underline S cheme and \underline M ulti-\underline P recision approach. Specifically, this is the first effort to assign mixed quantization schemes and multiple p…