32 citations · 107 across the 33 of their papers we have counts for
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cs.ET2020
Modeling Silicon-Photonic Neural Networks under Uncertainties
Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty
Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the ene…
cs.AR2020★ 1 cited
LORAX: Loss-Aware Approximations for Energy-Efficient Silicon Photonic Networks-on-Chip
Febin Sunny, Asif Mirza, Ishan Thakkar +2
The approximate computing paradigm advocates for relaxing accuracy goals in applications to improve energy-efficiency and performance. Recently, this paradigm has been explored to…