Convolutional neural network (CNN)

  • No Comments

A 7.5-μW 35-Keyword End-to-End Keyword Spotting System With Random Augmented On-Chip Training

A 7.5-μW 35-Keyword End-to-End Keyword Spotting System With Random Augmented On-Chip Training 150 150
Author(s): Hyuk-Jin Lee, Kyunghoon Pyo, Taekwang Jang, Mingoo Seok, SeongHwan Cho

Abstract:

Fully integrated keyword spotting (KWS) systems designed for low-power operation face two major challenges. First, increasing the number of supported keywords significantly raises system complexity and power consumption. Second, most existing systems are not personalized to individual users, as they are trained on data from native English speakers, leading to …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 10
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3660634
Publisher: IEEE

View on IEEE Xplore

  • No Comments

An Energy-Efficient CNN Processor Supporting Bi-Directional FPN for Small-Object Detection on High-Resolution Videos in 16-nm FinFET

An Energy-Efficient CNN Processor Supporting Bi-Directional FPN for Small-Object Detection on High-Resolution Videos in 16-nm FinFET 150 150
Author(s): Yu-Chun Ding, Chia-Yu Chang, Hui-Yun Tsai, Chun-Yeh Lin, Hao-Jiun Tu, Yu-Ching Su, Kai-Feng Chang, Tsung-Han Hsieh, Wen-Ching Chen, Nian-Shyang Chang, Chun-Pin Lin, Chi-Shi Chen, Chao-Tsung Huang

Abstract:

The capability to detect small objects precisely in real time is essential for intelligent systems, particularly in advanced driver assistance systems (ADASs), as it ensures continuous awareness of distant obstacles for enhanced safety. However, achieving high detection precision for small objects requires high-resolution input inference on deep convolutional neural network (…

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3628249
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A Multicore Programmable Variable-Precision Near-Memory Accelerator for CNN and Transformer Models

A Multicore Programmable Variable-Precision Near-Memory Accelerator for CNN and Transformer Models 150 150
Author(s): Yiming Yang, Yiyang Yuan, Xinghua Wang, Xiaoran Li, Hao Wu, Qihao Liu, Weiye Tang, Xiangqu Fu, Feng Zhang

Abstract:

Convolutional neural network (CNN) and transformer are the most popular neural network models in computer vision (CV) and natural language processing (NLP). It is quite common to use both these two models in multimodal scenarios, such as text-to-image generation. However, these two models have very different memory mappings, dataflows and …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3624011
Publisher: IEEE

View on IEEE Xplore

Search this website