# A 10.60 μW 150 GOPS Mixed-Bit-Width Sparse CNN Accelerator for Life-Threatening Ventricular Arrhythmia Detection

arXiv: https://arxiv.org/abs/2410.17395 · HTML: https://mlsanity.com/paper/2410.17395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 5 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Abstract (arXiv)

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 μW of power while achieving a performance of 150 GOPS and a diagnostic accuracy of 99.95%. The computation power density is only 0.57 μW/mm2, which is 14.23X smaller than state-of-the-art works, making it highly suitable for implantable and wearable medical devices.