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

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A Miniature Batteryless Bioelectronic Implant Using One Magnetoelectric Transducer for Wireless Powering and PWM Backscatter Communication 使用一个磁电传感器进行无线供电和 PWM 反向散射通信的微型无电池生物电子植入物。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-25 DOI: 10.1109/TBCAS.2024.3468374

Zhanghao Yu;Yiwei Zou;Huan-Cheng Liao;Fatima Alrashdan;Ziyuan Wen;Joshua E. Woods;Wei Wang;Jacob T. Robinson;Kaiyuan Yang

{"title":"A Miniature Batteryless Bioelectronic Implant Using One Magnetoelectric Transducer for Wireless Powering and PWM Backscatter Communication","authors":"Zhanghao Yu;Yiwei Zou;Huan-Cheng Liao;Fatima Alrashdan;Ziyuan Wen;Joshua E. Woods;Wei Wang;Jacob T. Robinson;Kaiyuan Yang","doi":"10.1109/TBCAS.2024.3468374","DOIUrl":"10.1109/TBCAS.2024.3468374","url":null,"abstract":"Wireless minimally invasive bioelectronic implants enable a wide range of applications in healthcare, medicine, and scientific research. Magnetoelectric (ME) wireless power transfer (WPT) has emerged as a promising approach for powering miniature bio-implants because of its remarkable efficiency, safety limit, and misalignment tolerance. However, achieving low-power and high-quality uplink communication using ME remains a challenge. This paper presents a pulse-width modulated (PWM) ME backscatter uplink communication enabled by a switched-capacitor energy extraction (SCEE) technique. The SCEE rapidly extracts and dissipates the kinetic energy within the ME transducer during its ringdown period, enabling time-domain PWM in ME backscatter. Various circuit techniques are presented to realize SCEE with low power consumption. This paper also describes the high-order modeling of ME transducers to facilitate the design and analysis, which shows good matching with measurement. Our prototyping system includes a millimeter-scale ME implant with a fully integrated system-on-chip (SoC) and a portable transceiver for power transfer and bidirectional communication. SCEE is proven to induce \u0000<inline-formula><tex-math>$>$</tex-math></inline-formula>\u0000 50% amplitude reduction within 2 ME cycles, leading to a PWM ME backscatter uplink with 17.73 kbps data rate and 0.9 pJ/bit efficiency. It also achieves 8.5\u0000<inline-formula><tex-math>$times$</tex-math></inline-formula>\u000010\u0000<sup>-5</sup>\u0000 bit-error-rate (BER) at a 5 cm distance, using a lightweight multi-layer-perception (MLP) decoding algorithm. Finally, the system demonstrates continuous wireless neural local-field potential (LFP) recording in an \u0000<italic>in vitro</i>\u0000 setup.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1197-1208"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335178","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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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 6-9 GHz 1.28 Gbps 76 mW Amplitude and Synchronized Time Shift Keying IR-UWB CMOS Transceiver for Brain Computer Interfaces 用于脑计算机接口的 6-9 GHz 1.28 Gbps 76 mW 振幅和同步时移键控 IR-UWB CMOS 收发器。

IEEE transactions on biomedical circuits and systems Pub Date : 2024-09-23 DOI: 10.1109/TBCAS.2024.3465533

Geunhaeng Lee;Junyoung Jang;Kyoungseok Song;Tae Wook Kim

{"title":"A 6-9 GHz 1.28 Gbps 76 mW Amplitude and Synchronized Time Shift Keying IR-UWB CMOS Transceiver for Brain Computer Interfaces","authors":"Geunhaeng Lee;Junyoung Jang;Kyoungseok Song;Tae Wook Kim","doi":"10.1109/TBCAS.2024.3465533","DOIUrl":"10.1109/TBCAS.2024.3465533","url":null,"abstract":"This paper proposes a low-power, high-speed impulse radio-ultra-wideband (IR-UWB) transceiver for brain computer interfaces (BCIs) using amplitude and synchronized time shift keying technique (ASTSK). The proposed IR-UWB transmitter (Tx) generates two pulses (sync pulse and data pulse) per symbol rate. The time difference between two pulses is used for synchronized time shift keying and the amplitude of the two pulses is used for amplitude shift keying. The receiver (Rx) demodulates the time difference with a low power time-to-digital converter (TDC) and peak detector (PD) based amplitude demodulation is suggested to relax analog-to-digital converter (ADC) burden for low power receiver. Especially the Tx-based synchronized operation eliminates the need for complex clock circuitry such as phase-lock loop (PLL) and reference crystal oscillator. Therefore, it can achieve low power and high-speed operation. The prototype, fabricated in 65 nm CMOS, has a frequency range of 6-9 GHz, communication speed of 1.28 Gbps, and power consumption of 18 mW (Tx) and 58 mW (Rx). This work is a fully integrated RF transceiver adapted for high-order modulation and designed to include the receiver.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 3","pages":"605-615"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309475","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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