@conference {47736,
	title = {Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems.},
	booktitle = { IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2016},
	month = {2016 Jun 15},
	address = {Clarion Congress Hotel in Prague Prague; Czech Republic},
	abstract = {<p>With recent advances in high-throughput Electron Microscopy (EM) imaging it is now possible to image an entire nervous system of organisms like Drosophila melanogaster. One of the bottlenecks to reconstruct a connectome from these large volumes ({\oe} 100 TiB) is the pixel-wise prediction of membranes. The time it would typically take to process such a volume using a convolutional neural network (CNN) with a sliding window approach is in the order of years on a current GPU. With sliding windows, however, a lot of redundant computations are carried out. In this paper, we present an extension to the Caffe library to increase throughput by predicting many pixels at once. On a sliding window network successfully used for membrane classification, we show that our method achieves a speedup of up to 57{\texttimes}, maintaining identical prediction results.</p>
},
	isbn = {978-147992350-2},
	doi = {10.1109/ISBI.2016.7493487},
	author = {Tschopp, F and Martel, J N P and Turaga, Srinivas C and Cook, M and Funke, Jan}
}
