Efficient and Encrypted Inference using Binarized Neural Networks within In-Memory Computing Architectures
It addresses security concerns for BNNs in resource-constrained applications like edge computing, though it is incremental as it builds on existing encryption and in-memory computing techniques.
This paper tackles the problem of protecting Binarized Neural Network (BNN) model parameters from theft in in-memory computing architectures by using a secret key from a physical unclonable function to encrypt weights, enabling inference on encrypted data with minimal overhead and reducing accuracy to below 15% without the key.
Binarized Neural Networks (BNNs) are a class of deep neural networks designed to utilize minimal computational resources, which drives their popularity across various applications. Recent studies highlight the potential of mapping BNN model parameters onto emerging non-volatile memory technologies, specifically using crossbar architectures, resulting in improved inference performance compared to traditional CMOS implementations. However, the common practice of protecting model parameters from theft attacks by storing them in an encrypted format and decrypting them at runtime introduces significant computational overhead, thus undermining the core principles of in-memory computing, which aim to integrate computation and storage. This paper presents a robust strategy for protecting BNN model parameters, particularly within in-memory computing frameworks. Our method utilizes a secret key derived from a physical unclonable function to transform model parameters prior to storage in the crossbar. Subsequently, the inference operations are performed on the encrypted weights, achieving a very special case of Fully Homomorphic Encryption (FHE) with minimal runtime overhead. Our analysis reveals that inference conducted without the secret key results in drastically diminished performance, with accuracy falling below 15%. These results validate the effectiveness of our protection strategy in securing BNNs within in-memory computing architectures while preserving computational efficiency.