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03/14/2023
Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers
Printed electronics (PE) promises on-demand fabrication, low non-recurri...
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02/28/2023
Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits
Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware...
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01/26/2023
Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons
The demand of many application domains for flexibility, stretchability, ...
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10/02/2022
Approximate Computing and the Efficient Machine Learning Expedition
Approximate computing (AxC) has been long accepted as a design alternati...
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07/25/2022
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Deep Neural Networks (DNNs) are being heavily utilized in modern applica...
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03/16/2022
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
Deep Neural Networks (DNNs) are very popular because of their high perfo...
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03/15/2022
Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits
Although Printed Electronics (PE) cannot compete with silicon-based syst...
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03/15/2022
Automated Design Approximation to Overcome Circuit Aging
Transistor aging phenomena manifest themselves as degradations in the ma...
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03/11/2022
Cross-Layer Approximation For Printed Machine Learning Circuits
Printed electronics (PE) feature low non-recurring engineering costs and...
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03/08/2022
AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch
Current state-of-the-art employs approximate multipliers to address the ...
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07/20/2021
Positive/Negative Approximate Multipliers for DNN Accelerators
Recent Deep Neural Networks (DNNs) managed to deliver superhuman accurac...
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03/08/2021
Reliability-Aware Quantization for Anti-Aging NPUs
Transistor aging is one of the major concerns that challenges designers ...
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02/18/2021