In secure sensor-based applications, generating unique and secure credentials is crucial for ensuring trust and privacy. Traditionally, pseudo-random number generators have been used for this purpose. However, biometric data, especially fingerprint features, has emerged as a robust alternative. This article proposes a dynamic approach for generating a privacy-preserving unique identity from fingerprint sensor data. The method isolates a region of interest (ROI) from the fingerprint image and extracts two feature sets: minutiae-based data and texture-based information. These features are combined and optimized to produce a hybrid feature vector containing discriminative and relevant attributes. A unique identity (termed a key) is then dynamically generated from this vector, characterized by reliability, revocability, unlinkability, and irreversibility. Extensive evaluations using fingerprint images from sensors of varying quality and resolution have demonstrated the method’s robustness. The generated identities have been statistically validated using NIST and Diehard test suites, confirming strong adherence to randomness requirements. Also, comprehensive security analyses have shown resilience against different adversarial attacks. Notably, the approach avoids storing biometric data or generated identities, enhancing privacy protection. These features make the proposed method ideal for secure sensor-based applications such as authentication, data storage security, and digital signature schemes.

Computing deep AI algorithms on traditional CPUs and GPUs brings several performance and energy pitfalls. Most of the emerging AI accelerators target only the inference phase of deep learning. There have been very limited attempts to design a full-fledged AI accelerator capable of both training and inference in real-time. It is due to the highly compute and memory intensive nature of the training phase. In this paper, we propose P-ReTI, a novel analog photonics AI accelerator. P-ReTI uses silicon microdisk-based convolution, photonic phase change memory-based memory, and dense-wavelength-division-multiplexing for energy-efficient and ultrafast deep learning in real-time. We evaluate P-ReTI using a commercial CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. Compared to the state-of-the-art, P-ReTI improves the CNN throughput, energy-efficiency, and computational efficiency by up to two orders of magnitude with trivial accuracy degradation.

The efficient execution of attention-based transformers and large language models on traditional CPUs and GPUs presents significant challenges related to performance and energy efficiency. While innovative solutions like ASICs, FPGAs, and ReRAMs have been explored, the field of silicon photonics has emerged as a promising avenue for developing energy-efficient accelerators for deep AI models. Notably, existing endeavors in silicon photonics have predominantly concentrated on inference for deep AI algorithms, leaving a limited number of initiatives focused on creating comprehensive deep learning accelerators capable of real-time training for transformer-like algorithms. This paper utilizes the superior merits of silicon photonics to realize a full-fledged transformer accelerator equipped for both inference and training. Introducing PHOTRAN, an AI analog photonics accelerator, we harness silicon microdisk-based convolution, photonic phase-change memory-based cache, and dense-wavelength-division-multiplexing to achieve energy-efficient and ultrafast transformer acceleration. Through evaluations using a commercial CAD framework on benchmark models, including Vision Transformers and Large Language models, our results showcase the superior performance of PHOTRAN. This work underscores the significant potential of photonic computing for on-chip training of large deep AI models.