Hadamard Domain Training with Integers for Class Incremental Quantized Learning

Autor: Schiemer, Martin, Schaefer, Clemens JS, Vap, Jayden Parker, Horeni, Mark James, Wang, Yu Emma, Ye, Juan, Joshi, Siddharth

Continual learning is a desirable feature in many modern machine learning applications, which allows in-field adaptation and updating, ranging from accommodating distribution shift, to fine-tuning, and to learning new tasks. For applications with pri

Externí odkaz: http://arxiv.org/abs/2310.03675

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

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

Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search

Autor: Schaefer, Clemens JS, Guo, Elfie, Stanton, Caitlin, Zhang, Xiaofan, Jablin, Tom, Lambert-Shirzad, Navid, Li, Jian, Chou, Chiachen, Joshi, Siddharth, Wang, Yu Emma

Serving large-scale machine learning (ML) models efficiently and with low latency has become challenging owing to increasing model size and complexity. Quantizing models can simultaneously reduce memory and compute requirements, facilitating their wi

Externí odkaz: http://arxiv.org/abs/2302.01382

Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks

Autor: Schaefer, Clemens JS, Joshi, Siddharth, Li, Shan, Blazquez, Raul

The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural netw

Externí odkaz: http://arxiv.org/abs/2206.07741

Modellierung und Analyse mobiler Architekturen

Autor: Gruhn, Volker, Schäfer, Clemens

Das Verhalten eines mobilen Systems wird bestimmt durch seine Architektur (statische und dynamische Anteile, Softwareverteilung), die zu Grunde liegende Netzwerkinfrastruktur (Topologie, Parameter wie Bandbreiten oder Latenzzeiten) und Interaktionen

Externí odkaz: https://ul.qucosa.de/id/qucosa%3A32884
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