Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Yousefzadeh, Amirreza"'
Currently, neural-network processing in machine learning applications relies on layer synchronization, whereby neurons in a layer aggregate incoming currents from all neurons in the preceding layer, before evaluating their activation function. This i
Externí odkaz:
http://arxiv.org/abs/2408.05098
Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair comparison is missing in the literature. In this work, we propose an ev
Externí odkaz:
http://arxiv.org/abs/2407.20421
Autor:
Arjmand, Cina, Xu, Yingfu, Shidqi, Kevin, Dobrita, Alexandra F., Vadivel, Kanishkan, Detterer, Paul, Sifalakis, Manolis, Yousefzadeh, Amirreza, Tang, Guangzhi
Neuromorphic processors are well-suited for efficiently handling sparse events from event-based cameras. However, they face significant challenges in the growth of computing demand and hardware costs as the input resolution increases. This paper prop
Externí odkaz:
http://arxiv.org/abs/2406.17483
Autor:
Dobrita, Alexandra, Yousefzadeh, Amirreza, Thorpe, Simon, Vadivel, Kanishkan, Detterer, Paul, Tang, Guangzhi, van Schaik, Gert-Jan, Konijnenburg, Mario, Gebregiorgis, Anteneh, Hamdioui, Said, Sifalakis, Manolis
For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environments. However, since maintaining low-latency and power-efficient infer
Externí odkaz:
http://arxiv.org/abs/2406.17285
Autor:
Patino-Saucedo, Alberto, Meijer, Roy, Yousefzadeh, Amirreza, Gomony, Manil-Dev, Corradi, Federico, Detteter, Paul, Garrido-Regife, Laura, Linares-Barranco, Bernabe, Sifalakis, Manolis
Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite their promising impact on performance and efficiency in tasks that exhib
Externí odkaz:
http://arxiv.org/abs/2404.10597
Autor:
Patiño-Saucedo, Alberto, Yousefzadeh, Amirreza, Tang, Guangzhi, Corradi, Federico, Linares-Barranco, Bernabé, Sifalakis, Manolis
The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANNs), the concept is almost absent. In their spiking neuroscience-inspir
Externí odkaz:
http://arxiv.org/abs/2309.05345
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
Autor:
Tang, Guangzhi, Safa, Ali, Shidqi, Kevin, Detterer, Paul, Traferro, Stefano, Konijnenburg, Mario, Sifalakis, Manolis, van Schaik, Gert-Jan, Yousefzadeh, Amirreza
Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algorithms, it isn't easy to properly benchmark and optimize their computati
Externí odkaz:
http://arxiv.org/abs/2303.15224
Activation sparsity improves compute efficiency and resource utilization in sparsity-aware neural network accelerators. As the predominant operation in DNNs is multiply-accumulate (MAC) of activations with weights to compute inner products, skipping
Externí odkaz:
http://arxiv.org/abs/2107.07305
Autor:
Gebregiorgis, Anteneh, Singh, Abhairaj, Yousefzadeh, Amirreza, Wouters, Dirk, Bishnoi, Rajendra, Catthoor, Francky, Hamdioui, Said
Publikováno v:
In Memories - Materials, Devices, Circuits and Systems July 2023 4