211 citations · 425 across the 12 of their papers we have counts for
6 papers · 1 filter
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu +1
Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time rema…
Deep counter networks for asynchronous event-based processing
Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer
Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same…
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Daniel Neil, Michael Pfeiffer, Shih-Chii Liu
Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. However, current RNN models are ill-suited to process irre…
Training Deep Spiking Neural Networks using Backpropagation
Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer
Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation. However, training…
Precise neural network computation with imprecise analog devices
Jonathan Binas, Daniel Neil, Giacomo Indiveri +2
The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency. Neve…
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen +16
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, includi…