A Constant-Time Implementation Methodology for Activation Functions on Microcontrollers
📰 ArXiv cs.AI
Learn to implement activation functions on microcontrollers in constant time to prevent timing side channel leaks, crucial for secure embedded neural network inference
Action Steps
- Implement branchless selection for activation functions using bitwise operations
- Apply fixed-cost Padé-based approximation for activation functions like ReLU, sigmoid, and tanh
- Validate the constant-time implementation on a microcontroller platform such as ARM Cortex-M4
- Compare the performance of different activation functions using the proposed methodology
- Configure the microcontroller to optimize the constant-time implementation for secure inference
Who Needs to Know This
Embedded systems engineers and AI researchers working on secure neural network inference can benefit from this methodology to prevent information leakage through timing side channels
Key Insight
💡 Constant-time implementation of activation functions can prevent information leakage through timing side channels in embedded neural network inference
Share This
🔒 Prevent timing side channel leaks in embedded neural networks with constant-time activation function implementation 🤖
Full Article
Title: A Constant-Time Implementation Methodology for Activation Functions on Microcontrollers
Abstract:
arXiv:2605.22441v1 Announce Type: cross Abstract: Embedded neural-network inference can leak information through timing side channels, including leakage caused by the evaluation of activation functions. This work proposes a constant-time implementation methodology for activation functions on embedded microcontrollers and validates it on ReLU, sigmoid, tanh, GELU, and Swish on an ARM Cortex-M4 platform. The proposed methodology combines branchless selection, fixed-cost Pad\'e-based approximation,
Abstract:
arXiv:2605.22441v1 Announce Type: cross Abstract: Embedded neural-network inference can leak information through timing side channels, including leakage caused by the evaluation of activation functions. This work proposes a constant-time implementation methodology for activation functions on embedded microcontrollers and validates it on ReLU, sigmoid, tanh, GELU, and Swish on an ARM Cortex-M4 platform. The proposed methodology combines branchless selection, fixed-cost Pad\'e-based approximation,
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