In this work, we show how a unique feature of PE, namely additive printing can be leveraged to perform in-situ tuning of pNNs to compensate accuracy losses ...
In this paper, we propose an in-situ (post-fabrication) tuning method for printed pNN to increase their accuracy under device variations. We take advantage of ...
In this work, we show how a unique feature of PE, namely additive printing can be leveraged to perform in-situ tuning of pNNs to compensate accuracy losses ...
Sep 23, 2024 · A quantized neural network designed for low bit-width (≤ 6-bit) training is used for simulations to demonstrate the potential of MLC storage.
In-situ Tuning of Printed Neural Networks for Variation Tolerance
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In-situ Tuning of Printed Neural Networks for Variation Tolerance. https://doi.org/10.23919/date54114.2022.9774591. Journal: 2022 Design, Automation & ...
Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction ...
In-situ Tuning of Printed Neural Networks for Variation Tolerance. Hefenbrock, Michael 1; Weller, Dennis D. 2; Aghassi-Hagmann, Jasmin ORCID iD icon 3; Beigl ...
In-situ tuning of printed neural networks for variation tolerance. M Hefenbrock, DD Weller, J Aghassi-Hagmann, M Beigl, MB Tahoori. 2022 Design, Automation ...
In this work, we propose a test pattern generation approach to detect fault patterns in DNNs' synaptic weight value representations at a bit level.
In-situ Tuning of Printed Neural Networks for Variation Tolerance. DATE 2022 ... Printed Stochastic Computing Neural Networks. DATE 2021: 914-919; 2020.