Ultra-low Power Analog Folded Neural Network for Cardiovascular Health Monitoring

A wearable heart guardian that can run with extremely low power and continuously check ECG signals.

Ultra-low Power Analog Folded Neural Network for Cardiovascular Health Monitoring

Overview

Most health wearables trade battery life for intelligence. This project redesigns that balance. We built an analog folded neural network that works like a compact, energy-sipping specialist for ECG patterns. Folded means the same hardware is reused cleverly over time, so we do more with less power and less chip area. For non-experts: imagine one skilled doctor examining patients one by one very efficiently instead of hiring a full hospital for every check. The result is continuous heart monitoring with tiny energy needs, opening the door to lighter, longer-lasting, and more practical preventive healthcare devices.

Real-World Impact

Makes always-on heart monitoring more realistic for everyday users, not just clinical environments.

Technologies & Techniques

Analog Neural NetworksFolded Neural NetworksUltra-Low Power DesignHealth MonitoringWearable ComputingECG Signal ProcessingDistributed SensingVLSI

Key Achievements

Near-batteryless operating profile through serialized computation

Optimized 6-layer model (hidden size 30) for ECG anomaly screening

Continuous monitoring suitable for day-long wearable use

Strong detection performance for key cardiovascular warning patterns

Low thermal noise and compact on-chip footprint

Lower peak power compared with conventional analog neural implementations

Publications

Citation data synced from scholarly cache.

Neural network design via voltage-based resistive processing unit and diode activation function-a new architecture

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

2021

Cited by 18

Read Publication →

Ultra-low Power Analog Recurrent Neural Network Design Approximation for Wireless Health Monitoring

2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

2022

Cited by 14

Read Publication →

Hybrid analog-digital sensing approach for low-power real-time anomaly detection in drones

2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

2021

Cited by 12

Read Publication →

Ultra-low power analog folded neural network for cardiovascular health monitoring

IEEE Journal of Biomedical and Health Informatics

2024

Cited by 6

Read Publication →

Want to Discuss This Research?

I'm always excited to discuss technical details, potential applications, or collaborative opportunities.