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• W4323022592 abstract "Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver. Nevertheless, large feature sizes in PE prohibit the realization of complex printed ML circuits. In this work, we present, for the first time, an automated printed-aware software/hardware co-design framework that exploits approximate computing principles to enable ultra-resource constrained printed multilayer perceptrons (MLPs). Our evaluation demonstrates that, compared to the state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area (power) and less than 1% accuracy loss." @default.
• W4323022592 created "2023-03-04" @default.
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• W4323022592 date "2023-09-01" @default.
• W4323022592 modified "2023-09-26" @default.
• W4323022592 title "Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits" @default.
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• W4323022592 doi "https://doi.org/10.1109/tc.2023.3251863" @default.
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