MINA: A Hardware-Efficient and Flexible Mini-InceptionNet Accelerator for ECG Classification in Wearable Devices

Classification is a crucial aspect of cardiovascular-related challenges, requiring thorough research and optimization to develop effective solutions for both patients and doctors. Recently, rapid advancements in artificial intelligence, particularly Convolutional Neural Networks (CNNs), have introduced numerous effective methods, significantly improving disease classification in Electrocardiogram (ECG) analysis. However, existing CNN-based accelerators often encounter challenges such as high parameter counts, limited flexibility in handling diverse CNN configurations, and inefficient hardware utilization. To address these issues, this paper proposes the Mini InceptionNet Accelerator (MINA), a hardware-efficient and flexible accelerator designed specifically for one-dimensional (1-D) CNN-based ECG classification. First, a novel 1-D CNN model, Mini InceptionNet, reduces the parameter count by 41.6% compared to the smallest existing 1-D CNN, minimizing memory requirements while maintaining high classification accuracy. Second, a flexible Processing Element Array (PEA) is designed with a Sharing Buffer Allocator (SBA) to support dynamic data coordination across various network topology parameters. Third, each Processing Element (PE) is equipped with four Local Data Memories (LDMs) and an ALU, enabling efficient intermediate data storage and versatile operations for modern CNN models. To demonstrate its effectiveness, MINA has been successfully implemented and verified on the ZCU102 FPGA at the system-on-chip level. FPGA evaluations show that MINA achieves 1.3×-2.9× higher energy efficiency (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GOP/s/MeLUT</i>) than state-of-the-art 2-D CNN accelerators. Compared to existing 1-D CNN accelerators, MINA achieves at least 1.53× improvement in the area-delay product (ADP). Additionally, weight pruning is discussed as a supporting strategy, achieving up to 3× faster inference time and a 2.13× improvement in ADP at 70% sparsity.