Niknia, Farzad, Wang, Ziheng, Liu, Shanshan, Reviriego, Pedro, Louri, Ahmed, and Lombardi, Fabrizio. ASIC Design of Nanoscale Artificial Neural Networks for Inference/Training by Floating-Point Arithmetic. Retrieved from https://par.nsf.gov/biblio/10588113. IEEE Transactions on Nanotechnology 23. Web. doi:10.1109/TNANO.2024.3367916.

Niknia, Farzad, Wang, Ziheng, Liu, Shanshan, Reviriego, Pedro, Louri, Ahmed, & Lombardi, Fabrizio. ASIC Design of Nanoscale Artificial Neural Networks for Inference/Training by Floating-Point Arithmetic. IEEE Transactions on Nanotechnology, 23 (). Retrieved from https://par.nsf.gov/biblio/10588113. https://doi.org/10.1109/TNANO.2024.3367916

Niknia, Farzad, Wang, Ziheng, Liu, Shanshan, Reviriego, Pedro, Louri, Ahmed, and Lombardi, Fabrizio. "ASIC Design of Nanoscale Artificial Neural Networks for Inference/Training by Floating-Point Arithmetic". IEEE Transactions on Nanotechnology 23 (). Country unknown/Code not available: IEEE. https://doi.org/10.1109/TNANO.2024.3367916. https://par.nsf.gov/biblio/10588113.

@article{osti_10588113,
place = {Country unknown/Code not available}, title = {ASIC Design of Nanoscale Artificial Neural Networks for Inference/Training by Floating-Point Arithmetic}, url = {https://par.nsf.gov/biblio/10588113}, DOI = {10.1109/TNANO.2024.3367916}, abstractNote = {}, journal = {IEEE Transactions on Nanotechnology}, volume = {23}, publisher = {IEEE}, author = {Niknia, Farzad and Wang, Ziheng and Liu, Shanshan and Reviriego, Pedro and Louri, Ahmed and Lombardi, Fabrizio}, }