In 2011, Schmidhuber's team at IDSIA with his postdoc Dan Ciresan also achieved dramatic speedups of convolutional neural networks (CNNs) on fast parallel computers called GPUs. An earlier CNN on GPU by Chellapilla et al. (2006) was 4 times faster than an equivalent implementation on CPU. The deep CNN of Dan Ciresan et al. (2011) at IDSIA was already 60 times faster and achieved the first superhuman performance in a computer vision contest in August 2011. Between 15 May 2011 and 10 September 2012, their fast and deep CNNs won no fewer than four image competitions. They also significantly improved on the best performance in the literature for multiple image databases. The approach has become central to the field of computer vision. It is based on CNN designs introduced much earlier by Yann LeCun et al. (1989) who applied the backpropagation algorithm to a variant of Kunihiko Fukushima's original CNN architecture called neocognitron, later modified by J. Weng's method called max-pooling.