Work-in-Progress: QRCNN: Scalable CNNs

Dropping the features/kernels in the convolutional layer of convolutional neural networks is a popular variant of structured pruning to reduce the computational load, but this comes at the cost of retraining and performance loss. In this work, we propose the QRCNN framework that shows graceful degradation of performance while dropping features in the convolutional layers without needing retraining. The framework allows trimming the network at inference time, thereby scaling with available computational power. The proposed method can achieve a reduction in the number of MAC computations of 1.22 – 2.28× with a median of 1.575×. The speedup is measured on three compute platforms namely 8GB RAM Raspberry Pi 4B (embedded platform), octa-core Intel i7 processor with 64GB RAM, and NVidia Quadro K2200 (GPU) with 4GB memory.