Brain-Computer Interfaces (BCIs) have the potential to restore motion for patients suffering from spinal cord injuries. Making such systems embedded, or even implantable, imposes strict low power constraints. Feature extraction, which transforms brain signals into intermediate representations before decoding motor intent, is typically the most compute intensive step. In this work, we introduce ALIFE-BCI, an Adaptive Quality Feature Extractor (AQFE), based on a Continuous Wavelet Transform (CWT) that captures the signal dynamics in both the time and frequency domains. The system is optimized with a top-down approach: (i) At the algorithmic level, it implements a piecewise linear approximation of the CWT that allows real-time energy-accuracy trade-offs. (ii) At the architectural level, memory reuse and parallelism are used to balance area and compute performance. (iii) At the circuit level, low-power techniques are used in a 22 nm FDSOI technology physical implementation flow. Three variants, with different levels of parallelism, are explored to extract 960 features at a rate of 10 Hz for a BCI motor application. The optimal variant, with an area of only 0.061 mm2, achieves 0.27 μW/feature at maximum quality, and 0.13 μW/feature at minimum quality, resulting in 8× lower power than existing digital solutions. Combined, these characteristics make the system well-suited for ultra-low-power implantable BCI decoders.

In clinical trials, brain signal decoders combined with spinal stimulation have shown to be a promising means to restore mobility to paraplegic and tetraplegic patients. To make this technology available for home use, the complex brain signal decoding must be performed using a low-power, portable battery operated system. This case study shows how the decoding algorithm for a Brain-Computer Interface (BCI) system was ported to an embedded platform, resulting in an over 25 x power reduction, compared to the previous implementation, while respecting real-time and accuracy constraints.

With Fifth Generation (5G) mobile networks, the concept of Bandwidth Part (BWP) was introduced to support flexible Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier spacing, also deemed numerologies to achieve lower latency for delay stringent services. A BWP is a set of contiguous Physical Resource Blocks (PRBs) associated to a numerology which is scanned by the User Equipment (UE) to retrieve or submit its data. Additionally, a UE can be connected to multiple slices or services simultaneously thanks to slicing which was introduced in 5G to ensure isolation among multiple co-existing services on the same network. In this context, a BWP switch is required for such users when their services use different numerologies. In this paper, we propose three innovative methods to optimize the BWP Switching process for users connected to multiple slices where both enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC) services are considered. These mechanisms rely on the modification of the Downlink Control Information (DCI) which carries the BWP indicator for users as well as the BWP Inactivity Timer that triggers a Default BWP switch. The performance evaluation demonstrates the high efficiency of our solutions in terms of latency for URLLC services, throughput for eMBB services and number of DCIs scanned compared to the baseline approach.