Alapfogalmak
ApproxDARTS, a neural architecture search method, integrates approximate multipliers into the popular DARTS algorithm to enable the design of energy-efficient deep neural networks.
Kivonat
The paper presents ApproxDARTS, a neural architecture search (NAS) method that combines the DARTS algorithm with the use of approximate multipliers. The key highlights are:
ApproxDARTS extends the DARTS method to enable the use of approximate multipliers in the convolutional layers of the generated neural networks. This is achieved by leveraging the TFApprox4IL framework, which provides support for approximate multipliers.
The architecture search stage of ApproxDARTS explores the search space of neural network architectures that can utilize approximate multipliers. The final architecture is then evaluated in the second stage, where the full training and testing is performed.
Experiments on the CIFAR-10 dataset show that ApproxDARTS can produce competitive convolutional neural networks (CNNs) containing approximate multipliers, with a negligible accuracy drop (< 1.3%) compared to the baseline using exact 32-bit floating-point multipliers.
The CNNs generated by ApproxDARTS demonstrate significant energy savings in the arithmetic operations during inference, with a 53.84% reduction when using the approximate mul8u_NGR multiplier and a 5.97% reduction when using the exact 8-bit fixed-point multiplier, compared to the baseline.
ApproxDARTS is shown to be 2.3x faster than the similar EvoApproxNAS method, which also integrates approximate multipliers into the neural architecture search process.
Overall, ApproxDARTS provides an efficient way to design energy-efficient deep neural networks by incorporating approximate computing principles directly into the neural architecture search process.
Statisztikák
The paper does not provide any specific numerical data or statistics to support the key logics. The results are presented in terms of classification accuracy, number of parameters, and energy consumption reduction compared to the baseline.
Idézetek
The paper does not contain any striking quotes that support the key logics.