Increasing the Energy-Efficiency of Wearables Using Low-Precision Posit Arithmetic with PHEE

2025-01-30

AI Summary

Energy efficiency bottlenecks hinder the deployment of wearable health monitoring devices. Method: This work proposes a high-energy-efficiency computing architecture leveraging low-precision Posit number representations—specifically 8-, 10-, and 16-bit Posits—tailored for wearable medical applications. We design PHEE, a scalable RISC-V processor, and Coprosit, a dedicated Posit-accelerating coprocessor, implemented and verified on the X-HEEP framework in TSMC 16 nm CMOS technology. Contribution/Results: For cough detection and ECG R-peak detection, 16-bit Posits incur negligible accuracy loss, while R-peak detection sustains full accuracy at only 8 bits. Compared to IEEE 754 32-bit floating-point, the Posit-based functional units reduce area by 38% and energy consumption by 54%, with no performance degradation. This work establishes a novel computing paradigm that jointly ensures clinical-grade accuracy and hardware efficiency, enabling miniaturized, long-battery-life wearable biomedical systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Biometrics, Face, Gesture & Pose
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Wearable biomedical devices are increasingly being used for continuous patient health monitoring, enabling real-time insights and extended data collection without the need for prolonged hospital stays. These devices must be energy efficient to minimize battery size, improve comfort, and reduce recharging intervals. This paper investigates the use of specialized low-precision arithmetic formats to enhance the energy efficiency of biomedical wearables. Specifically, we explore posit arithmetic, a floating-point-like representation, in two key applications: cough detection for chronic cough monitoring and R peak detection in ECG analysis. Simulations reveal that 16-bit posits can replace 32-bit IEEE 754 floating point numbers with minimal accuracy loss in cough detection. For R peak detection, posit arithmetic achieves satisfactory accuracy with as few as 10 or 8 bits, compared to the 16-bit requirement for floating-point formats. To further this exploration, we introduce PHEE, a modular and extensible architecture that integrates the Coprosit posit coprocessor within a RISC-V-based system. Using the X-HEEP framework, PHEE seamlessly incorporates posit arithmetic, demonstrating reduced hardware area and power consumption compared to a floating-point counterpart system. Post-synthesis results targeting 16nm TSMC technology show that the posit hardware targeting these biomedical applications can be 38% smaller and consume up to 54% less energy at the functional unit level, with no performance compromise. These findings establish the potential of low-precision posit arithmetic to significantly improve the energy efficiency of wearable biomedical devices.

Problem

• Energy Efficiency
• Wearable Health Monitoring
• Battery Size

Innovation

• Posit Arithmetic
• PHEE Architecture
• Energy Efficiency