Training Neural Networks for Execution on Approximate Hardware
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Abstract (arXiv)
Approximate computing methods have shown great potential for deep learning. Due to the reduced hardware costs, these methods are especially suitable for inference tasks on battery-operated devices that are constrained by their power budget. However, approximate computing hasn't reached its full potential due to the lack of work on training methods. In this work, we discuss training methods for approximate hardware. We demonstrate how training needs to be specialized for approximate hardware, and propose methods to speed up the training process by up to 18X.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 8 Apr 2023.
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7 of 24 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
- 2016Training Deep Nets with Sublinear Memory Costcited by 848
- 2014cuDNN: Efficient Primitives for Deep Learningcited by 245
- 2018CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUscited by 121
- 2022ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inferencecited by 9
- 2022PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Acceleratorcited by 1
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