Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Lenz, Gregor"'
Autor:
Lenz, Gregor, McLelland, Douglas
Transmitting Earth observation image data from satellites to ground stations incurs significant costs in terms of power and bandwidth. For maritime ship detection, on-board data processing can identify ships and reduce the amount of data sent to the
Externí odkaz:
http://arxiv.org/abs/2406.11319
Autor:
Pedersen, Jens E., Abreu, Steven, Jobst, Matthias, Lenz, Gregor, Fra, Vittorio, Bauer, Felix C., Muir, Dylan R., Zhou, Peng, Vogginger, Bernhard, Heckel, Kade, Urgese, Gianvito, Shankar, Sadasivan, Stewart, Terrence C., Sheik, Sadique, Eshraghian, Jason K.
Publikováno v:
Nat Commun 15, 8122 (2024)
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neur
Externí odkaz:
http://arxiv.org/abs/2311.14641
Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to the power consumed when executed on neuromorphic hardware. Training s
Externí odkaz:
http://arxiv.org/abs/2309.16795
Autor:
Yik, Jason, Berghe, Korneel Van den, Blanken, Douwe den, Bouhadjar, Younes, Fabre, Maxime, Hueber, Paul, Kleyko, Denis, Pacik-Nelson, Noah, Sun, Pao-Sheng Vincent, Tang, Guangzhi, Wang, Shenqi, Zhou, Biyan, Ahmed, Soikat Hasan, Joseph, George Vathakkattil, Leto, Benedetto, Micheli, Aurora, Mishra, Anurag Kumar, Lenz, Gregor, Sun, Tao, Ahmed, Zergham, Akl, Mahmoud, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Bogdan, Petrut, Bohte, Sander, Buckley, Sonia, Cauwenberghs, Gert, Chicca, Elisabetta, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Fischer, Tobias, Forest, Jeremy, Fra, Vittorio, Furber, Steve, Furlong, P. Michael, Gilpin, William, Gilra, Aditya, Gonzalez, Hector A., Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Liu, Yao-Hong, Liu, Shih-Chii, Ma, Haoyuan, Manohar, Rajit, Margarit-Taulé, Josep Maria, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Panda, Priyadarshini, Park, Jongkil, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Pierro, Alessandro, Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens JS, van Schaik, André, Schemmel, Johannes, Schmidgall, Samuel, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Shrestha, Sumit Bam, Sifalakis, Manolis, Sironi, Amos, Stewart, Matthew, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Timcheck, Jonathan, Tömen, Nergis, Urgese, Gianvito, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Zohora, Fatima Tuz, Frenkel, Charlotte, Reddi, Vijay Janapa
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accu
Externí odkaz:
http://arxiv.org/abs/2304.04640
Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of-the-art back-propagation through time (BPTT) is, however, very time-c
Externí odkaz:
http://arxiv.org/abs/2205.10242
We present the first publicly available Android framework to stream data from an event camera directly to a mobile phone. Today's mobile devices handle a wider range of workloads than ever before and they incorporate a growing gamut of sensors that m
Externí odkaz:
http://arxiv.org/abs/2205.06836
We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our contribution is sub
Externí odkaz:
http://arxiv.org/abs/2112.14793
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks model such event-based data and develop their full energy-saving potentia
Externí odkaz:
http://arxiv.org/abs/2110.02929
Autor:
Eshraghian, Jason K., Ward, Max, Neftci, Emre, Wang, Xinxin, Lenz, Gregor, Dwivedi, Girish, Bennamoun, Mohammed, Jeong, Doo Seok, Lu, Wei D.
The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial an
Externí odkaz:
http://arxiv.org/abs/2109.12894
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