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Deep neural networks (DNNs) have become ubiquitous in machine learning, buttheir energy consumption remains a notable issue. Lowering the supply voltageis an effective strategy for reducing energy consumption. However, aggressivelyscaling down the supply voltage can lead to accuracy degradation due to randombit flips in static random access memory (SRAM) where model parameters arestored. To address this challenge, we introduce NeuralFuse, a novel add-onmodule that addresses the accuracy-energy tradeoff in low-voltage regimes bylearning input transformations to generate error-resistant datarepresentations. NeuralFuse protects DNN accuracy in both nominal andlow-voltage scenarios. Moreover, NeuralFuse is easy to implement and can bereadily applied to DNNs with limited access, such as non-configurable hardwareor remote access to cloud-based APIs. Experimental results demonstrate that, ata 1% bit error rate, NeuralFuse can reduce SRAM memory access energy by up to24% while improving accuracy by up to 57%. To the best of our knowledge, thisis the first model-agnostic approach (i.e., no model retraining) to addresslow-voltage-induced bit errors. The source code is available athttps://github.com/IBM/NeuralFuse.

NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes