Wearable edge AI 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 edge AI biomedical wearables. Specifically, we explore posit arithmetic, a floating-point-like representation, in two biomedical applications that leverage supervised and unsupervised learning algorithms: cough detection for chronic cough monitoring and R peak detection in ECG analysis. Our results 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 validate these findings beyond algorithm-level simulations, 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 serves as a proof-of-concept platform to quantify the practical energy benefits of low-precision posits in edge AI systems. Post-synthesis results targeting 16 nm TSMC technology show that the posit hardware targeting these ML-based biomedical applications can be 38% smaller and consume up to 42.3% less power 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 edge AI biomedical devices.

翻译：可穿戴边缘AI生物医学设备越来越多地用于患者持续健康监测，无需长期住院即可实现实时洞察和扩展数据收集。这些设备必须具有高能效，以最小化电池尺寸、提高舒适度并减少充电间隔。本文研究了专用低精度算术格式在提升边缘AI生物医学可穿戴设备能效方面的应用。具体而言，我们探索了类似浮点表示的Posit算术在两种利用监督和无监督学习算法的生物医学应用中的效果：慢性咳嗽监测中的咳嗽检测以及心电图分析中的R波峰值检测。结果显示，16位Posit可取代32位IEEE 754浮点数，在咳嗽检测中仅引入极小精度损失。对于R波峰值检测，Posit算术仅需10或8位即可获得满意精度，而浮点格式至少需要16位。为在算法级仿真之外验证这些发现，我们提出了PHEE，一种模块化可扩展架构，在基于RISC-V的系统中集成了Coprosit Posit协处理器。利用X-HEEP框架，PHEE作为概念验证平台，量化了边缘AI系统中低精度Posit的实际能效优势。针对16纳米台积电技术的综合后结果表明，针对这些基于机器学习的生物医学应用的Posit硬件在功能单元层面可缩小38%，功耗降低高达42.3%，且性能不受影响。这些发现确立了低精度Posit算术显著提升边缘AI生物医学设备能效的潜力。