A 28.2 μC Neuromorphic Sensing System Featuring SNN-based Near-sensor Computation and Event-Driven Body-Channel Communication for Insertable Cardiac Monitoring

He, Yuming; Corradi, Federico; Shi, Chengyao; Ding, Ming; Timmermans, Martijn; Stuijt, Jan; Harpe, Pieter; Ocket, Ilja; Liu, Yao‐Hong

doi:10.1109/a-sscc53895.2021.9634787

Cited by 10 publications

(12 citation statements)

References 7 publications

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“…In Table I the presented impulse-based galvanic coupled BCC is benchmarked with stateof-the-art high-speed (>200 Mbps) and miniature implantable transmitters (Tx), including inductive coupling, optical and Impulse-radio UWB (IR-UWB). The proposed GC-BCC demonstrates a data rate up to 250 Mbps with a small electronic area of 26 mm 2 , while most state-of-the-art GC-BCC communication [34], [35], [49] has limited bandwidth up to 10's MHz. Inductive coupled communication can achieve a higher data rate with a "de-Q" technique [23], but it suffers from high path loss and still requires a relatively large coil area.…”

Section: Discussionmentioning

confidence: 99%

“…Galvanic-coupled body channel communications (GC-BCCs) reported in the literature focus primarily on-body to on-body [29]- [32] or on-body to implant [33], [34] communication using skin-attached electrodes with relatively large dimensions, which does not well represent the channel properties of implant-to-implant trans-dural communication in this work. Although galvanic coupling has been employed for implant-to-implant intra-cardiac communication [35], it only demonstrates an operation frequency below MHz for the ECG application.…”

Section: Introductionmentioning

confidence: 99%

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Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing

Shi

Song

Gao

et al. 2022

IEEE Trans. Microwave Theory Techn.

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A digital-impulse galvanic coupling as a new high-speed trans-dural (from cortex to the skull) data transmission method has been presented in this paper. The proposed wireless telemetry replaces the tethered wires connected in between implants on the cortex and above the skull, allowing the brain implant to be "free-floating" for minimizing brain tissue damage. Such trans-dural wireless telemetry must have a wide channel bandwidth for high-speed data transfer and a small form factor for minimum invasiveness. To investigate the propagation property of the channel, a finite element model is developed and a channel characterization based on a liquid phantom and porcine tissue is performed. The results show that the trans-dural channel has a wide frequency response of up to 250 MHz. Propagation loss due to micro-motion and misalignments is also investigated in this work. The result indicates that the proposed transmission method is relatively insensitive to misalignment. It has approximately 1 dB extra loss when there is a horizontal misalignment of 1mm. A pulse-based transmitter ASIC and a miniature PCB module are designed and validated ex-vivo with a 10-mm thick porcine tissue. This work demonstrates a high-speed and miniature in-body galvanic-coupled pulse-based communication with a data rate up to 250 Mbps with an energy efficiency of 2 pJ/bit, and has a small module area of only 26 mm 2 .

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing

Shi

Song

Gao

et al. 2022

IEEE Trans. Microwave Theory Techn.

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“…Hence, the use of LC-ADCs can result in up to two orders of magnitude reductions of data bandwidth and system energy consumption, especially when input signals are burst-like or sparse in time [18,22]. Since many biological signals, such as electrocardiogram (ECG) and electroencephalogram (EEG) data, exhibit burst-like sparsity properties, a lot of attention has been paid to the application of LC-ADCs for ultra-low-power biomedical monitoring applications [8].…”

Section: Introductionmentioning

confidence: 99%

Exploring Information-Theoretic Criteria to Accelerate the Tuning of Neuromorphic Level-Crossing ADCs

Safa

Assche

Frenkel

et al. 2023

Neuro-Inspired Computational Elements Conference

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Level-crossing analog-to-digital converters (LC-ADCs) are neuromorphic, event-driven data converters that are gaining much attention for resource-constrained applications where intelligent sensing must be provided at the extreme edge, with tight energy and area budgets. LC-ADCs translate real-world analog signals (such as ECG, EEG, etc.) into sparse spiking signals, providing significant data bandwidth reduction and inducing savings of up to two orders of magnitude in area and energy consumption at the system level compared to the use of conventional ADCs. In addition, the spiking nature of LC-ADCs make their use a natural choice for ultra-low-power, event-driven spiking neural networks (SNNs). Still, the compressed nature of LC-ADC spiking signals can jeopardize the performance of downstream tasks such as signal classification accuracy, which is highly sensitive to the LC-ADC tuning parameters. In this paper, we explore the use of popular information criteria found in model selection theory for the tuning of the LC-ADC parameters. We experimentally demonstrate that information metrics such as the Bayesian, Akaike and corrected Akaike criteria can be used to tune the LC-ADC parameters in order to maximize downstream SNN classification accuracy. We conduct our experiments using both full-resolution weights and 4bit quantized SNNs, on two different bio-signal classification tasks. We believe that our findings can accelerate the tuning of LC-ADC parameters without resorting to computationally-expensive grid searches that require many SNN training passes.

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“…Fig. 3(b) shows this proposed neuromorphic sensing system (NSS) concept [13]. It includes an ASC for delta encoding, a spiking neural network (SNN) for local computation, and a pulse-based TX tailored for low-energy, event-driven transmission.…”

Section: Introductionmentioning

confidence: 99%

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

Corradi

Shi

et al. 2022

IEEE J. Solid-State Circuits

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A 28.2 μC Neuromorphic Sensing System Featuring SNN-based Near-sensor Computation and Event-Driven Body-Channel Communication for Insertable Cardiac Monitoring

Cited by 10 publications

References 7 publications

Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing

Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing

Exploring Information-Theoretic Criteria to Accelerate the Tuning of Neuromorphic Level-Crossing ADCs

An Implantable Neuromorphic Sensing System Featuring Near-Sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

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