Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Schaefer, Clemens JS"'
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
Autor:
Schaefer, Clemens JS, Lambert-Shirzad, Navid, Zhang, Xiaofan, Chou, Chiachen, Jablin, Tom, Li, Jian, Guo, Elfie, Stanton, Caitlin, Joshi, Siddharth, Wang, Yu Emma
Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution which simultaneously reduces memory footprint and compute requirement
Externí odkaz:
http://arxiv.org/abs/2306.04879
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
Energy efficient implementations and deployments of Spiking neural networks (SNNs) have been of great interest due to the possibility of developing artificial systems that can achieve the computational powers and energy efficiency of the biological b
Externí odkaz:
http://arxiv.org/abs/2302.04174
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
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
Autor:
Enciso, Zephan M., Mirfarshbafan, Seyed Hadi, Castañeda, Oscar, Schaefer, Clemens JS., Studer, Christoph, Joshi, Siddharth
Spatial linear transforms that process multiple parallel analog signals to simplify downstream signal processing find widespread use in multi-antenna communication systems, machine learning inference, data compression, audio and ultrasound applicatio
Externí odkaz:
http://arxiv.org/abs/2009.07332
Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators
The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation targeting energy-efficient digital accelerators have been investigated in
Externí odkaz:
http://arxiv.org/abs/2003.11639
Autor:
Yik, Jason, Ahmed, Soikat Hasan, Ahmed, Zergham, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Blanken, Douwe den, Bogdan, Petrut, Bohte, Sander, Bouhadjar, Younes, Buckley, Sonia, Cauwenberghs, Gert, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Forest, Jeremy, Furber, Steve, Furlong, Michael, Gilra, Aditya, Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Lenz, Gregor, Manohar, Rajit, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Pacik-Nelson, Noah, Panda, Priyadarshini, Pao-Sheng, Sun, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens JS, van Schaik, André, Schemmel, Johannes, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Shrestha, Sumit Bam, Sifalakis, Manolis, Sironi, Amos, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Tang, Guangzhi, Timcheck, Jonathan, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Zhou, Biyan, Zohora, Fatima Tuz, Frenkel, Charlotte, Reddi, Vijay Janapa
Publikováno v:
arXiv, 2023:2304.04640v2. Cornell University Library
The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a
Autor:
Schaefer, Clemens JS, Joshi, Siddharth
Publikováno v:
ACM International Conference Proceeding Series; 7/28/2020, p1-8, 8p