Wearable biosignal processing applications are driving significant progress
toward miniaturized, energy-efficient Internet-of-Things solutions for both
clinical and consumer applications. However, scaling toward high-density
multi-channel front-ends is only feasible by performing data processing and
machine Learning (ML) near-sensor through energy-efficient edge processing. To
tackle these challenges, we introduce BioGAP, a novel, compact, modular, and
lightweight (6g) medical-grade biosignal acquisition and processing platform
powered by GAP9, a ten-core ultra-low-power SoC designed for efficient
multi-precision (from FP to aggressively quantized integer) processing, as
required for advanced ML and DSP. BioGAPs form factor is 16x21x14 mm3 and
comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless
Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an
accelerometer; and a shield including an analog front-end (AFE) for ExG
acquisition. Finally, the system also includes a flexibly placeable
photoplethysmogram (PPG) PCB with a size of 9x7x3 mm3 and a rechargeable
battery (Ï• 12x5 mm2). We demonstrate BioGAP on a Steady State Visually
Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We
achieve 3.6 uJ/sample in streaming and 2.2 uJ/sample in onboard processing
mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW
with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW
allowing for an operation time of 15 h.Comment: 7 pages, 9 figures, 1 table, accepted for IEEE COINS 202