title: NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural
    Network Inference in Low-Voltage Regimes
summary: Deep neural networks (DNNs) have become ubiquitous in machine learning, but
  their energy consumption remains a notable issue. Lowering the supply voltage
  is an effective strategy for reducing energy consumption. However, aggressively
  scaling down the supply voltage can lead to accuracy degradation due to random
  bit flips in static random access memory (SRAM) where model parameters are
  stored. To address this challenge, we introduce NeuralFuse, a novel add-on
  module that addresses the accuracy-energy tradeoff in low-voltage regimes by
  learning input transformations to generate error-resistant data
  representations. NeuralFuse protects DNN accuracy in both nominal and
  low-voltage scenarios. Moreover, NeuralFuse is easy to implement and can be
  readily applied to DNNs with limited access, such as non-configurable hardware
  or remote access to cloud-based APIs. Experimental results demonstrate that, at
  a 1% bit error rate, NeuralFuse can reduce SRAM memory access energy by up to
  24% while improving accuracy by up to 57%. To the best of our knowledge, this
  is the first model-agnostic approach (i.e., no model retraining) to address
  low-voltage-induced bit errors. The source code is available at
  https://github.com/IBM/NeuralFuse.
paper_authors._id: 649e5ed43a0cf1c70dd20356
paper_authors.name: Hao-Lun Sun
paper_authors._id: 649e5ed43a0cf1c70dd20357
paper_authors.name: Lei Hsiung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-30T20:01:10.481Z
paper_authors._id: 62b3f106cb0eb32770775ef5
paper_authors.avatarUrl: /avatars/e5c61ebf4325584c51ccfda96b85b9b3.svg
paper_authors.fullname: Lei Hsiung
paper_authors.type: user
paper_authors.user: hsiung
paper_authors._id: 649e5ed43a0cf1c70dd20358
paper_authors.name: Nandhini Chandramoorthy
paper_authors._id: 649e5ed43a0cf1c70dd20359
paper_authors.name: Pin-Yu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-30T20:03:27.616Z
paper_authors._id: 6495dd0b71f6708e0f990032
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6495dd0b71f6708e0f990032/PBIjdKNnpkxvR_3djCGVm.png
paper_authors.fullname: Pin-Yu Chen
paper_authors.type: user
paper_authors.user: pinyuchen
paper_authors._id: 649e5ed43a0cf1c70dd2035a
paper_authors.name: Tsung-Yi Ho
paper_title: NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural
    Network Inference in Low-Voltage Regimes
paper_summary: Deep neural networks (DNNs) have become ubiquitous in machine learning, but
  their energy consumption remains a notable issue. Lowering the supply voltage
  is an effective strategy for reducing energy consumption. However, aggressively
  scaling down the supply voltage can lead to accuracy degradation due to random
  bit flips in static random access memory (SRAM) where model parameters are
  stored. To address this challenge, we introduce NeuralFuse, a novel add-on
  module that addresses the accuracy-energy tradeoff in low-voltage regimes by
  learning input transformations to generate error-resistant data
  representations. NeuralFuse protects DNN accuracy in both nominal and
  low-voltage scenarios. Moreover, NeuralFuse is easy to implement and can be
  readily applied to DNNs with limited access, such as non-configurable hardware
  or remote access to cloud-based APIs. Experimental results demonstrate that, at
  a 1% bit error rate, NeuralFuse can reduce SRAM memory access energy by up to
  24% while improving accuracy by up to 57%. To the best of our knowledge, this
  is the first model-agnostic approach (i.e., no model retraining) to address
  low-voltage-induced bit errors. The source code is available at
  https://github.com/IBM/NeuralFuse.
paper_ai_keywords: NeuralFuse Deep neural networks (DNNs) supply voltage energy consumption bit flips static random access memory (SRAM) model parameters input transformations error-resistant data representations low-voltage regimes