You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We also examine the speedup on real hardware, i.e. an off-the-shelf ARM-based computing core. The ShuffleNet model achieves \sim13\times actual speedup (theoretical speedup is 18\times) over AlexNet [21] while maintaining comparable accuracy. Table 6 compares our ShuffleNet with a few popular models. Results show that with similar accuracy ShuffleNet is much more efficient than others. For example, ShuffleNet 0.5\times is theoretically 18\times faster than AlexNet [21] with comparable classification score. We will evaluate the actual running time in Sec 4.5. Finally, we evaluate the actual inference speed of ShuffleNet models on a mobile device with an ARM platform. Though ShuffleNets with larger group numbers (e.g. g=4 or g=8) usually have better performance, we find it less efficient in our current implementation. Empirically g=3 usually has a proper trade-off between accuracy and actual inference time. As shown in Table 8, three input resolutions are exploited for the test. Due to memory access and other overheads, we find every 4\times theoretical complexity reduction usually results in \sim2.6\times actual speedup in our implementation. Nevertheless, compared with AlexNet [21] our ShuffleNet 0.5\times model still achieves \sim13\times actual speedup under comparable classification accuracy (the theoretical speedup is 18\times), which is much faster than previous AlexNet-level models or speedup approaches such as  [14, 16, 22, 42, 43, 38].

Question: Is the theoretical speedup greater than the actual speedup when comparing ShuffleNet to AlexNet on real hardware?
Yes, while the theoretical speedup of ShuffleNet is 18 times, the actual speedup is only ~13 times, compared to the AlexNet on the real hardware.