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Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Deep Neural Networks (DNNs) are being heavily utilized in modern applications and are putting energy-constraint devices to the test.
Positive/Negative Approximate Multipliers for DNN Accelerators
In addition, we propose a filter-oriented approximation method to map the weights to the appropriate modes of the approximate multiplier.
Reliability-Aware Quantization for Anti-Aging NPUs
Transistor aging is one of the major concerns that challenges designers in advanced technologies.
Control Variate Approximation for DNN Accelerators
In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators.
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