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× 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 20.8μ 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.

Level-crossing analog-To-digital converters (LC-ADCs) are neuromorphic, event-driven data converters that are gaining much attention for resource-constrained applications where intelligent sensing must be provided at the extreme edge, with tight energy and area budgets. LC-ADCs translate real-world analog signals (such as ECG, EEG, etc.) into sparse spiking signals, providing significant data bandwidth reduction and inducing savings of up to two orders of magnitude in area and energy consumption at the system level compared to the use of conventional ADCs. In addition, the spiking nature of LC-ADCs make their use a natural choice for ultra-low-power, event-driven spiking neural networks (SNNs). Still, the compressed nature of LC-ADC spiking signals can jeopardize the performance of downstream tasks such as signal classification accuracy, which is highly sensitive to the LC-ADC tuning parameters. In this paper, we explore the use of popular information criteria found in model selection theory for the tuning of the LC-ADC parameters. We experimentally demonstrate that information metrics such as the Bayesian, Akaike and corrected Akaike criteria can be used to tune the LC-ADC parameters in order to maximize downstream SNN classification accuracy. We conduct our experiments using both full-resolution weights and 4-bit quantized SNNs, on two different bio-signal classification tasks. We believe that our findings can accelerate the tuning of LC-ADC parameters without resorting to computationally-expensive grid searches that require many SNN training passes.