no code implementations • 19 Apr 2022 • Asif Mirza, Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty, Sudeep Pasricha, Mahdi Nikdast
Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93. 95% for the MNIST handwritten digit dataset -- considered as an example in this paper -- which corresponds to a <0. 5% accuracy loss compared to the variation-free case.
no code implementations • 12 Jul 2021 • Febin P. Sunny, Asif Mirza, Mahdi Nikdast, Sudeep Pasricha
However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead.
no code implementations • 13 Feb 2021 • Febin Sunny, Asif Mirza, Mahdi Nikdast, Sudeep Pasricha
Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and inference performance compared to CPUs and GPUs.
