Abstract
This paper introduces a low-power 90nm CMOS binary weight spiking neural network (BW-SNN) ASIC for real-time image classification. The chip maximizes data reuse through systolic arrays that house the entire 5-layer BW-SNN, requiring a minimum off-chip bandwidth for data access. The chip achieves 97.57% accuracy for real-time bottled-drink recognition, consuming only 0.62uJ per inference. For comparison purpose, it achieves 98.73% accuracy for MNIST hand-written character recognition, consuming only 0.59uJ per inference. The bottled-drink recognition is demonstrated at 300 fps that is well enough for many other real-time applications. The peak efficiency point is 103.14TOPS/W at a voltage of 0.6V, which outperforms other designs so far as we know. By normalizing to the 28nm technology node, the proposed ASIC is about 5× more efficient and 7× lower hardware cost as compared with the state-of-the-art designs.