NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural
  Network Inference in Low-Voltage Regimes
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.
649e5ed43a0cf1c70dd20356
Hao-Lun Sun
649e5ed43a0cf1c70dd20357
Lei Hsiung
admin_assigned
2023-06-30T20:01:10.481Z
62b3f106cb0eb32770775ef5
/avatars/e5c61ebf4325584c51ccfda96b85b9b3.svg
Lei Hsiung
user
hsiung
649e5ed43a0cf1c70dd20358
Nandhini Chandramoorthy
649e5ed43a0cf1c70dd20359
Pin-Yu Chen
admin_assigned
2023-06-30T20:03:27.616Z
6495dd0b71f6708e0f990032
https://cdn-avatars.huggingface.co/v1/production/uploads/6495dd0b71f6708e0f990032/PBIjdKNnpkxvR_3djCGVm.png
Pin-Yu Chen
user
pinyuchen
649e5ed43a0cf1c70dd2035a
Tsung-Yi Ho
NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural
  Network Inference in Low-Voltage Regimes
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.
NeuralFuse is a model-agnostic add-on for deep neural networks that mitigates low-voltage-induced bit errors in SRAM, reducing memory access energy and improving accuracy without retraining.
NeuralFuse deep neural networks energy consumption supply voltage SRAM model parameters bit flips error-resistant data representations low-voltage memory access energy