IEEE transactions on biomedical circuits and systems最新文献_第7页

EPOC: A 28-nm 5.3 pJ/SOP Event-Driven Parallel Neuromorphic Hardware With Neuromodulation-Based Online Learning EPOC：基于神经调制在线学习的 28 纳米 5.3 pJ/SOP 事件驱动并行神经形态硬件。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-10-02 DOI: 10.1109/TBCAS.2024.3470520

Faquan Chen;Qingyang Tian;Lisheng Xie;Yifan Zhou;Ziren Wu;Liangshun Wu;Rendong Ying;Fei Wen;Peilin Liu

{"title":"EPOC: A 28-nm 5.3 pJ/SOP Event-Driven Parallel Neuromorphic Hardware With Neuromodulation-Based Online Learning","authors":"Faquan Chen;Qingyang Tian;Lisheng Xie;Yifan Zhou;Ziren Wu;Liangshun Wu;Rendong Ying;Fei Wen;Peilin Liu","doi":"10.1109/TBCAS.2024.3470520","DOIUrl":"10.1109/TBCAS.2024.3470520","url":null,"abstract":"Bio-inspired neuromorphic hardware with learning ability is highly promising to achieve human-like intelligence, particularly in terms of high energy efficiency and strong environmental adaptability. Though many customized prototypes have demonstrated learning ability, learning on neuromorphic hardware still lacks a bio-plausible and unified learning framework, and inherent spike-based sparsity and parallelism have not been fully exploited, which fundamentally limits their computational efficiency and scale. Therefore, we develop a unified, event-driven, and massively parallel multi-core neuromorphic online learning processor, namely EPOC. We present a neuromodulation-based neuromorphic online learning framework to unify various learning algorithms, and EPOC supports high-accuracy local/global supervised Spike Neural Network (SNN) learning with a low-memory-demand streaming single-sample learning strategy through different neuromodulator formulations. EPOC leverages a novel event-driven computation method that fully exploits spike-based sparsity throughout the forward-backward learning phases, and parallel multi-channel and multi-core computing architecture, bringing 9.9<inline-formula><tex-math>$times$</tex-math></inline-formula> time efficiency improvement compared with the baseline architecture. We synthesize EPOC in a 28-nm CMOS process and perform extensive benchmarking. EPOC achieves state-of-the-art learning accuracy of 99.2%, 98.2%, and 94.3% on the MNIST, NMNIST, and DVS-Gesture benchmarks, respectively. Local-learning EPOC achieves 2.9<inline-formula><tex-math>$times$</tex-math></inline-formula> time efficiency improvement compared with the global learning counterpart. EPOC operates at a typical clock frequency of 100 MHz, providing a peak 328 GOPS/51 GSOPS throughput and a 5.3 pJ/SOP energy efficiency.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"629-644"},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE 生物医学电路与系统论文集》出版信息

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-26 DOI: 10.1109/TBCAS.2024.3463213

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TechRxiv: Share Your Preprint Research with the World! TechRxiv：与世界分享您的预印本研究成果！

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-26 DOI: 10.1109/TBCAS.2024.3464773

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Together, We are advance technology 我们共同推动技术进步

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-26 DOI: 10.1109/TBCAS.2024.3464777

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IEEE Circuits and Systems Society Information 电气和电子工程师学会电路与系统协会信息

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-26 DOI: 10.1109/TBCAS.2024.3464769

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Blank Page 空白页

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-26 DOI: 10.1109/TBCAS.2024.3464771

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ACE: Automated Optimization Towards Iterative Classification in Edge Health Monitors ACE：边缘健康监测器中实现迭代分类的自动优化

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-25 DOI: 10.1109/TBCAS.2024.3468160

Yuxuan Wang;Lara Orlandic;Simone Machetti;Giovanni Ansaloni;David Atienza

{"title":"ACE: Automated Optimization Towards Iterative Classification in Edge Health Monitors","authors":"Yuxuan Wang;Lara Orlandic;Simone Machetti;Giovanni Ansaloni;David Atienza","doi":"10.1109/TBCAS.2024.3468160","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3468160","url":null,"abstract":"Wearable devices for health monitoring are essential for tracking individuals’ health status and facilitating early detection of diseases. However, processing biomedical signals online for real-time monitoring is challenging due to limited computational resources on edge devices. To address this challenge, we propose an application-agnostic methodology called ACE (Automated optimization towards classification on the Edge). ACE converts a health monitoring algorithm with feature extraction and classification into an iterative detection process, incorporating algorithms of varying complexities and minimizing re-computation of shared data. First, ACE decomposes a monolithic model, employing a single feature set and classifier, into multiple algorithms with different computational complexities. Then, our automatic analysis tool integrates buffering logic into these algorithms to prevent re-computation of shared computational-intensive data. The optimized algorithm is then converted into a low-level language in C for deployment. During runtime, the system initiates monitoring with the lowest complexity algorithm and iteratively involves algorithms with higher complexity without recomputing the existing data. The iteration process continues until a pre-defined confidence threshold is met. We demonstrate the effectiveness of ACE on two biomedical applications: seizure detection and emotional state classification. ACE achieves at least 28.9% and 18.9% runtime savings without any accuracy loss on a Cortex-A9 edge platform for the two benchmarks, respectively. We discuss and demonstrate how ACE can be used by designers of such biomedical algorithms to automatically optimize and deploy their applications on the edge.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"82-92"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A Multi-Bit ECRAM-Based Analog Neuromorphic System With High-Precision Current Readout Achieving 97.3% Inference Accuracy 基于多位 ECRAM 的模拟神经形态系统，具有高精度电流读取功能，推理精确度达 97.3%。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-23 DOI: 10.1109/TBCAS.2024.3465610

Minseong Um;Minil Kang;Kyeongho Eom;Hyunjeong Kwak;Kyungmi Noh;Jimin Lee;Jeonghoon Son;Jiseok Kwon;Seyoung Kim;Hyung-Min Lee

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A Cyto-Silicon Hybrid System with On-Chip Closed-Loop Modulation 具有片上闭环调制功能的细胞硅混合系统

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-23 DOI: 10.1109/TBCAS.2024.3466549

Jun Wang;Seok Joo Kim;Wenxuan Wu;Jongha Lee;Henry Hinton;Rona S. Gertner;Han Sae Jung;Hongkun Park;Donhee Ham

{"title":"A Cyto-Silicon Hybrid System with On-Chip Closed-Loop Modulation","authors":"Jun Wang;Seok Joo Kim;Wenxuan Wu;Jongha Lee;Henry Hinton;Rona S. Gertner;Han Sae Jung;Hongkun Park;Donhee Ham","doi":"10.1109/TBCAS.2024.3466549","DOIUrl":"10.1109/TBCAS.2024.3466549","url":null,"abstract":"We introduce a bioelectronic interface between biological electrogenic cells and a mixed-signal CMOS integrated circuit with an array of surface electrodes, where not only is the CMOS electrode array capable of electrophysiological recording and stimulation of the cells with 1,024 recording and stimulation channels, but it can also provide low-latency artificial signal pathways from cells it records to cells it stimulates. This on-chip closed-loop modulation has an intrinsic latency less than 5 µs. To demonstrate the utility of the on-chip closed loop modulation as an artificial feedback pathway between biological cells, we develop a silicon-cardiomyocyte self-sustained oscillator with a tunable frequency to which both the relevant part of the CMOS chip and cells are locked, and also a silicon-neuron interface with a silicon inhibitory connection between neuronal cells. This line of cyto-silicon hybrid system, where the boundary between biological and semiconductor systems is blurred, may find applications in prosthesis, brain-machine interface, and fundamental biology research.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"577-589"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Design and Implementation of Integrated Dual-Mode Pulse and Continuous-Wave Electron Paramagnetic Resonance Spectrometers 集成双模脉冲和连续波电子顺磁共振频谱仪的设计与实现。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-20 DOI: 10.1109/TBCAS.2024.3465210

Jui-Hung Sun;Difei Wu;Peter Qin;Constantine Sideris

{"title":"Design and Implementation of Integrated Dual-Mode Pulse and Continuous-Wave Electron Paramagnetic Resonance Spectrometers","authors":"Jui-Hung Sun;Difei Wu;Peter Qin;Constantine Sideris","doi":"10.1109/TBCAS.2024.3465210","DOIUrl":"10.1109/TBCAS.2024.3465210","url":null,"abstract":"Electron paramagnetic resonance (EPR) is a powerful spectroscopic technique that allows direct detection and characterization of radicals containing unpaired electron(s). The development of portable, low-power EPR sensing modalities has the potential to significantly expand the utility of EPR in a broad range of fields, ranging from basic science to practical applications such as point-of-care diagnostics. The two major methodologies of EPR are continuous-wave (CW) EPR, where the frequency or field is swept with a constant excitation, and pulse EPR, where short pulses induce a transient signal. In this work, we present the first realization of a fully integrated pulse EPR spectrometer on-chip. The spectrometer utilizes a subharmonic direct-conversion architecture that enables an on-chip oscillator to be used as a dual-mode EPR sensing cell, capable of both CW and pulse-mode operation. An on-chip reference oscillator is used to injection-lock the sensor to form pulses and also to downconvert the pulse EPR signal. A proof-of-concept spectrometer IC with two independent sensing cells is presented, which achieves a pulse sensitivity of \u0000<inline-formula><tex-math>$4.6times 10^{9}$</tex-math></inline-formula>\u0000 spins (1000 averages) and a CW sensitivity of \u0000<inline-formula><tex-math>$2.9times 10^{9}$</tex-math></inline-formula>\u0000 spins/\u0000<inline-formula><tex-math>$sqrt{text{Hz}}$</tex-math></inline-formula>\u0000 and can be powered and controlled via a computer USB interface. The sensing cells consume as little as 2.1mW (CW mode), and the system is tunable over a wide frequency range of 12.8–14.9GHz (CW/pulse). Single-pulse free induction decay (FID), two-pulse inversion recovery, two-pulse Hahn echo, three-pulse stimulated echo, and CW experiments demonstrate the viability of the spectrometer for use in portable EPR sensing.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1209-1219"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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