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Arxiv
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MAx-DNN: Multi-Level Arithmetic Approximation for Energy-Efficient DNN Hardware Accelerators
- The paper titled 'MAx-DNN' explores the use of fine-grained error resilience and hardware approximation techniques for energy-efficient Deep Neural Network (DNN) computing.
- It focuses on utilizing approximate multipliers distributed at different levels within the network to achieve higher energy efficiency with acceptable accuracy levels.
- Experiments conducted on the ResNet-8 model using the CIFAR-10 dataset showed up to 54% energy gains at the cost of up to 4% accuracy loss compared to the baseline model, and 2x energy gains with improved accuracy compared to current DNN approximations.
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