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EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network Acceleration

arXiv 2402.18595 ↗First posted 25 Feb 2024cs.AR cs.CE cs.LGcited by 0

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Abstract (arXiv)

Deep neural networks (DNNs) have achieved great breakthroughs in many fields such as image classification and natural language processing. However, the execution of DNNs needs to conduct massive numbers of multiply-accumulate (MAC) operations on hardware and thus incurs a large power consumption. To address this challenge, we propose a novel digital MAC design based on encoding. In this new design, the multipliers are replaced by simple logic gates to represent the results with a wide bit representation. The outputs of the new multipliers are added by bit-wise weighted accumulation and the accumulation results are compatible with existing computing platforms accelerating neural networks. Since the multiplication function is replaced by a simple logic representation, the critical paths in the resulting circuits become much shorter. Correspondingly, pipelining stages and intermediate registers used to store partial sums in the MAC array can be reduced, leading to a significantly smaller area as well as better power efficiency. The proposed design has been synthesized and verified by ResNet18- Cifar10, ResNet20-Cifar100, ResNet50-ImageNet, MobileNetV2-Cifar10, MobileNetV2-Cifar100, and EfficientNetB0-ImageNet. The experimental results confirmed the reduction of circuit area by up to 48.79% and the reduction of power consumption of executing DNNs by up to 64.41%, while the accuracy of the neural networks can still be well maintained. The open source code of this work can be found on GitHub with link https://github.com/Bo-Liu-TUM/EncodingNet/.

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References · 20

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  12. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
8 more
  1. 2016Convolutional Neural Networks using Logarithmic Data Representationcited by 97
  2. 2019A Survey on Neural Architecture Searchcited by 71
  3. 2022Hardware Approximate Techniques for Deep Neural Network Accelerators: A Surveycited by 39
  4. 2021DKM: Differentiable K-Means Clustering Layer for Neural Network Compressioncited by 31
  5. 2023Class-Aware Pruning for Efficient Neural Networkscited by 9
  6. 2023PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Accelerationcited by 8
  7. 2023Early-Exit with Class Exclusion for Efficient Inference of Neural Networkscited by 8
  8. 2023OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillationcited by 5

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