A fully-mapped and energy-efficient FPGA accelerator for dual-function AI-based analysis of ECG

Abstract

In this paper, a fully-mapped field programmable gate array (FPGA) accelerator is proposed for artificial intelligence (AI)-based analysis of electrocardiogram (ECG). It consists of a fully-mapped 1-D convolutional neural network (CNN) and a fully-mapped heart rate estimator, which constitute a complementary dual-function analysis. The fully-mapped design projects each layer of the 1-D CNN to a hardware module on an Intel Cyclone V FPGA, and a virtual flatten layer is proposed to effectively bridge the feature extraction layers and fully-connected layer. Also, the fully-mapped design maximizes computational parallelism to accelerate CNN inference. For the fully-mapped heart rate estimator, it performs pipelined transformations, self-adaptive threshold calculation, and heartbeat count on the FPGA, without multiplexed usage of hardware resources. Furthermore, heart rate calculation is elaborately analyzed and optimized to remove division and acceleration, resulting in an efficient method suitable for hardware implementation. According to our experiments on 1-D CNN, the accelerator can achieve 43.08× and 8.38× speedup compared with the software implementations on ARM-Cortex A53 quad-core processor and Intel Core i7-8700 CPU, respectively. For the heart rate estimator, the hardware implementations are 25.48× and 1.55× faster than the software implementations on the two aforementioned platforms. Surprisingly, the accelerator achieves an energy efficiency of 63.48 GOPS/W, which obviously surpasses existing studies. Considering its power consumption is only 67.74 mW, it may be more suitable for resource-limited applications, such as wearable and portable devices for ECG monitoring.

Harnessing FPGA Technology for Enhanced Biomedical Computation

“…Studies have demonstrated the successful implementation of CNNs on FPGAs, achieving high accuracy in real-time patient monitoring systems. RNNs and LSTMs extend this capability by processing the temporal characteristics of ECG signals, where the sequence and timing of heartbeats provide insights into cardiac rhythm and identify abnormalities [17] [19]. Furthermore, the integration of FP-GAs in ECG signal analysis has facilitated the development of modern implantable cardiac devices such as pacemakers, where their high-performance and energy-efficient characteristics are crucial [20].…”

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease

“…Hardware acceleration significantly impacts disease detection techniques in several ways, providing faster processing, improved accuracy, deployment of real-time applications, scalability, and edge computing. An FPGA accelerator is proposed for an AI-based analysis with an electrocardiogram signal to monitor the heart condition [31]. A onedimensional convolutional neural network has been implemented using the hardware accelerator.…”