A 10.60 $μ$W 150 GOPS Mixed-Bit-Width Sparse CNN Accelerator for Life-Threatening Ventricular Arrhythmia Detection
This enables highly efficient implantable and wearable medical devices for real-time health monitoring, representing a domain-specific incremental improvement.
The paper tackled the problem of detecting life-threatening ventricular arrhythmia by proposing an ultra-low power mixed-bit-width sparse CNN accelerator, achieving 99.95% diagnostic accuracy with 10.60 μW power consumption and 150 GOPS performance.
This paper proposes an ultra-low power, mixed-bit-width sparse convolutional neural network (CNN) accelerator to accelerate ventricular arrhythmia (VA) detection. The chip achieves 50% sparsity in a quantized 1D CNN using a sparse processing element (SPE) architecture. Measurement on the prototype chip TSMC 40nm CMOS low-power (LP) process for the VA classification task demonstrates that it consumes 10.60 $μ$W of power while achieving a performance of 150 GOPS and a diagnostic accuracy of 99.95%. The computation power density is only 0.57 $μ$W/mm$^2$, which is 14.23X smaller than state-of-the-art works, making it highly suitable for implantable and wearable medical devices.