7 TOPS/W Cellular Neural Network Processor Core for Intelligent Internet-of-Things

Villemur, Martin; Julián, P.; Figliolia, Tomas; Andreou, Andreas G.

doi:10.1109/tcsii.2019.2933723

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Traditionally, cellular neural network topologies have been implemented in VLSI technology, particularly for image processing in early 90s [40] and more recently for real-time signal processing at high precision in <ms/frame in applications such as integrated sensing, autonomous vehicles, and mobile robots. [41][42][43] Recently, few papers have explored the implementation of such cellular topologies using nonvolatile memory devices which promise better performance and energy consumption. [44,45] A hybrid CMOS/memristor implementation was proposed for a standard cellular processing unit where the memristor matrix performs the weight-and-sum operation in the analog domain.…”

Section: Discussionmentioning

confidence: 99%

Hardware‐Mappable Cellular Neural Networks for Distributed Wavefront Detection in Next‐Generation Cardiac Implants

Yang

Zhang

Aras

et al. 2022

Advanced Intelligent Systems

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Machine learning (ML) algorithms are being adopted to analyze medical data in specialties like radiology, oncology, and cardiology, promising faster interpretation with accuracy close to doctors' diagnostics. [1] The next frontier in computing technology is to bring these powerful algorithms to implantable medical devices, which requires automation of real-time life-saving therapeutic decisions without the physician's presence. An example is the need for improved medical solutions for life-saving cardiac defibrillation therapies, that can detect bioelectric anomalies (e.g., cardiac arrhythmias) and act on this data locally for real-time therapy delivered within tens of seconds or minutes since the onset of life-threatening ventricular fibrillation (VF). The statistics put this challenging technological need in perspective: ventricular arrhythmias such as VF are responsible for over 700 000 sudden cardiac deaths a year in the USA and Europe. [2] VF is a common, life-threatening arrhythmia characterized by chaotic asynchronous electrical activity of the cardiac muscle, which results in death within 10 minutes.Individual differences in physiological mechanisms, anatomic and genetic determinants, and etiologies of various arrhythmias impact the course of treatment. Ablation therapy, while promising, remains a work in progress. Therefore, on average, defibrillation therapy delivered by implantable cardioverter defibrillators (ICDs) remains the most effective treatment as antiarrhythmic drugs have limited efficacy and can be associated with adverse side effects. Implants have to be biocompatible, organ conformal, and small enough to minimize the tissue damage and be capable of independent autonomous operation without external intervention. Low power is an essential characteristic to avoid the heat damage to the tissue and prolong the lifetime of the embedded battery for many years without recharging. [3] Currently, most volume of the ICD has been occupied by batteries, which has limited the volume reduction and the computing capacity. ICD has local computing based on a microprocessor to detect and differentiate arrhythmia to offer different treatments, but the resolution provided by ICD is really low typically limited to only one or a couple of sensors; as such, the ability to detect arrhythmia wavefronts is non-existent. The data can be read wirelessly by the physician during periodic checkups. Increasing the sensing resolution is desired but the local computing capacity has to also be increased which is difficult due to power constraints. Wireless data transmission for processing of data outside of the body is not a viable solution either, as real-time data transfer between

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