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ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers
Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and highly optimized hardware parameters such as low latency or inference energy.
Evolutionary Neural Architecture Search Supporting Approximate Multipliers
During the NAS process, a suitable CNN architecture is evolved together with approximate multipliers to deliver the best trade-offs between the accuracy, network size, and power consumption.
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