Field Programmable Neural Array for Biomedical Signal Processing

Processing biomedical signals using artificial intelligence (AI) algorithms, particularly convolutional neural networks (CNNs), presents challenges for resource-limited systems, such as smart patches. For instance, screening electrocardiogram (ECG) signals for heart arrhythmias, including atrial fibrillation (AF), is crucial for enhancing early diagnosis, as this condition is closely linked to an increased risk of stroke and overall mortality. Common approaches for System-on-Chip (SoC) implementation typically involve either a software-based solution utilizing a standard microcontroller or a dedicated hardware accelerator designed to execute the full CNN. This study investigates an approach utilizing reconfigurable fabric, including a theoretical model for efficiency estimation, similar to field programmable gate arrays (FPGAs). These specialized field programmable neural arrays (FPNAs) incorporate specialized tiles designed for AI inference. For instance, in the case of a single convolution, the FPNA occupies only half the silicon area while maintaining equivalent timing performance and achieving a 52.9 % reduction in energy consumption per inference. Further results are presented for a test CNN and a CNN for ECG processing. Although the FPNA may require more area and, at times, more energy per inference than a direct hardware implementation, this trade-off is deemed acceptable due to the advantages of reconfigurability.