Despite their tremendous successes, convolutional neural networks (CNNs)\nincur high computational/storage costs and are vulnerable to adversarial\nperturbations. Recent works on robust model compression address these\nchallenges by combining model compression techniques with adversarial training.\nBut these methods are unable to improve throughput (frames-per-second) on\nreal-life hardware while simultaneously preserving robustness to adversarial\nperturbations. To overcome this problem, we propose the method of …