FREYA: A 0.023-mm²/Channel, 20.8- μW/Channel, Event-Driven 8-Channel SoC for Spiking End-to-End Sensing of Time-Sparse Biosignals
Year: 2024
Type: article
Abstract: Biomedical systems-on-chip (SoCs) for real-time monitoring of vital signs need to read out multiple recording channels in parallel and process them locally with low latency, at a low per-channel area and power consumption. To achieve this, event-driven SoCs that exploit the time-sparse nature of biosignals such as the electrocardiogram (ECG) have been proposed; they only process the signal when it shows activity. Such SoCs convert time-sparse biosignals into spike trains, on which spiking neural networks (SNNs) can perform event-driven signal classification. State-of-the-art event-driven SoCs, however, still suffer from poor area and power efficiency and use inflexible, hard-coded spike-encoding schemes. To improve on these challenges, this paper presents FREYA, an 8-channel event-driven SoC for end-to-end sensing of time-sparse biosignals. The proposed SoC consists of the following key contributions: 1) an 8-channel time-division-multiplexed level-crossing sampling (LCS) analog-to-spike converter (ASC) that encodes analog input signals into input spikes for an on-chip SNN; 2) an ASC spike-encoding algorithm that is fully programmable in resolution (4 to 8 bits) and conversion algorithm (offset and decay parameters); 3) an on-chip integrated, flexible SNN processor based on a programmable crossbar architecture, that allows for efficient event-driven processing, and that can be reconfigured towards multiple sensing applications; 4) a custom offline end-to-end training framework for the fast retraining of the spike-encoding algorithm and SNN architecture towards new applications or patient-dependent signal variations. A prototype IC has been fabricated in a 40nm CMOS technology. It has a per-channel active area of 0.023 mm2 (0.184 mm2 in total), a$7\times $improvement over the state of the art. For the use case of ECG-based QRS-labeling, a detection accuracy of 98.67% is achieved, while the system consumes$20.8~\mu $W per channel and achieves a latency of only 80 ms, thus paving the way for multi-channel, high-fidelity, event-driven SoCs in biomedical applications.
Institutions: KU Leuven, Delft University of Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Fonds Wetenschappelijk Onderzoek
OpenAlex ID: W4404809432