Convolution

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PANDA: A Reconfigurable Seizure Prediction and Detection Neural Network Accelerator for Epilepsy Monitoring

PANDA: A Reconfigurable Seizure Prediction and Detection Neural Network Accelerator for Epilepsy Monitoring 150 150
Author(s): Siyuan Qiu, Xiaoyi Song, Xiangjie Song, Changchun Zhou, Xiaoyan Song, Jie Yang, Wenjin Wang, Yuchao Yang, Hailong Jiao

Abstract:

For effective healthcare management of epilepsy patients in a non-hospital environment, continuous monitoring of EEG signals is crucial, where wearable intelligent devices are essential. The existing seizure monitoring devices, however, cannot achieve high sensitivity, a short detection latency, a low false alarm rate (FAR), as well as ultra-low-power computing simultaneously. …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3699609
Publisher: IEEE

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LUT-Based Convolutional Tsetlin Machine Accelerator With Dynamic Clause Scaling for Resources-Constrained FPGAs

LUT-Based Convolutional Tsetlin Machine Accelerator With Dynamic Clause Scaling for Resources-Constrained FPGAs 150 150
Author(s): Rashed Al Amin, Roman Obermaisser

Abstract:

The rapid growth of machine learning (ML) workloads, particularly in computer vision applications, has significantly increased computational and energy demands in modern electronic systems, motivating the use of hardware accelerators to offload processing from general-purpose processors. Despite advances in computationally efficient ML models, achieving energy-efficient inference on resource-constrained edge devices …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 45 – 55
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3676833
Publisher: IEEE

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EMO-CIM: An Input/Stationary-Data Similarity-Aware Computing-In-Memory Design for Variable Vector-Wise Computation in Edge Multioperator AI Acceleration

EMO-CIM: An Input/Stationary-Data Similarity-Aware Computing-In-Memory Design for Variable Vector-Wise Computation in Edge Multioperator AI Acceleration 150 150
Author(s): Bo Wang, Yuchen Tang, Feiran Liu, Xiaoxue Zhong, Yuchen Ma, Xin Si, Jun Yang

Abstract:

We propose an edge multioperator computing-in-memory (EMO-CIM) design that supports variable vector-wise multiply-and-accumulate (MAC) in CNN, Depthwise (DW)-Convolution, and Attention operators. It features: 1) a single EMO-CIM bank (ECB) excels in variable vector-wise MAC (V-MAC) for multioperators; 2) merging local input-shared compute units (LISCUs) with a decode-unit and adder-tree (DUAT) facilitates …

Published in: IEEE Solid-State Circuits Letters
Page(s): 97 – 100
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3668853
Publisher: IEEE

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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

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MixCIM: A Hybrid Computing-in-Memory Macro With Less Data-Movement and Better Memory-Reuse for Depthwise Separable Neural Networks

MixCIM: A Hybrid Computing-in-Memory Macro With Less Data-Movement and Better Memory-Reuse for Depthwise Separable Neural Networks 150 150
Author(s): Xin Qiao, Youming Yang, Jiahao Song, Renjie Wei, Xiyuan Tang, Meng Li, Runsheng Wang, Yuan Wang

Abstract:

Computing-in-memory (CIM) architectures have demonstrated strong potential for edge artificial intelligence (AI) devices due to their enhanced parallelism and energy efficiency. With the growing complexity of AI tasks and the rapid increase in model size, computation and deployment costs have surged. Depthwise separable neural networks (DSNNs) have attracted interest for …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3651775
Publisher: IEEE

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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

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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

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