Hardware/software approach for code synchronization in low-power multi-core sensor nodes

Abstract

Abstract-Latest embedded bio-signal analysis applications, targeting low-power Wireless Body Sensor Nodes (WBSNs), present conflicting requirements. On one hand, bio-signal analysis applications are continuously increasing their demand for high computing capabilities. On the other hand, long-term signal processing in WBSNs must be provided within their highly constrained energy budget. In this context, parallel processing effectively increases the power efficiency of WBSNs, but only if the execution can be properly synchronized among computing elements. To address this challenge, in this work we propose a hardware/software approach to synchronize the execution of bio-signal processing applications in multi-core WBSNs. This new approach requires little hardware resources and very few adaptations in the source code. Moreover, it provides the necessary flexibility to execute applications with an arbitrarily large degree of complexity and parallelism, enabling considerable reductions in power consumption for all multi-core WBSN execution conditions. Experimental results show that a multi-core WBSN architecture using the illustrated approach can obtain energy savings of up to 40%, with respect to an equivalent singlecore architecture, when performing advanced bio-signal analysis.

A Synchronization-Based Hybrid-Memory Multi-Core Architecture for Energy-Efficient Biomedical Signal Processing

“…As an illustrative example, Figure 1 (left) reports the energy breakdown of a system performing an ECG features extraction application (named 3L-MMD in [8]). Shown data assumes that samples are acquired by the low-voltage ADC described in [7], processing is performed by the architecture proposed in [8] and a Bluetooth Low Energy protocol is used for wireless transmission [9].…”

“…It allows the efficient management of Single Instruction-Multiple Data (SIMD) execution and producer/consumer relationships among processors. Moreover, it Energy consumption breakdown of a smart WBSNs executing the 3-input features extraction application described in [8]. Right: scheme of a typical bio-signal DSP application (top) and its mapping on a ULP multi-core WBSN architecture (bottom).…”

“…Shown data assumes that samples are acquired by the low-voltage ADC described in [7], processing is performed by the architecture proposed in [8] and a Bluetooth Low Energy protocol is used for wireless transmission [9]. The breakdown highlights that the energy bottleneck resides in the embedded DSP stage, which is often the case in the smart WBSN scenario.…”

“…To maximize the overall efficiency of smart WBSNs, signal processing must therefore be supported within a tight power budget, while at the same time respecting real time constraints. In this regard, many efforts have been made in the last years, proposing solutions ranging from ad-hoc accelerators [10], [11] to ultra-low-power (ULP) multi-core architectures [8], [12].…”

Inexact‐aware architecture design for ultra‐low power bio‐signal analysis

“…These wearable devices for bio-signal monitoring are bringing about a revolutionary change in healthcare systems by allowing long-term monitoring of chronic patients, while providing a low-cost and unobtrusive solution. Wireless body sensor nodes (WBSNs) [3], represented in Fig. 1, are the building blocks of such a network, since they can provide real-time and personalised monitoring of patients.…”