Here, we introduce an optical computing method using free-space optics and a 4f system to enhance and integrate data processing, encryption, and machine learning. We propose a Reconfigurable Complex Convolution Module (RCCM) which enables simultaneous amplitude and phase modulation of optical signals for complex convolution operations in the Fourier domain. Utilizing spatial light modulators and interferometric techniques based on the Michelson interferometer, the RCCM achieves precise control over light properties. The system demonstrates promising applications in optical hashing, data compression, and accelerating machine learning tasks, particularly for processing encrypted data. Experimental results show the RCCM’s ability to perform complex convolutions with high accuracy, though trade-offs between compression ratios and classification accuracy are observed. This research represents a significant advancement in optical computing, addressing challenges in data security, processing speed, and computational efficiency across various fields.
Recent advancements in optical communications have explored the use of spatially structured beams, especially orbital angular momentum (OAM) beams, to achieve high-capacity data transmission. Traditional electronic convolutional neural networks (CNNs), while effective, face significant challenges in demultiplexing OAM beams efficiently, notably their high power consumption and substantial computational time, which can limit realtime processing capabilities in high-speed optical communication systems. In this study, we propose a hybrid optical-electronic CNN that integrates Fourier optics convolution for intensity recognition-based demultiplexing of multiplexed OAM beams under simulated atmospheric turbulence. Experimental results showed that the proposed hybrid neural network system achieves a 69% demultiplexing accuracy under strong turbulence conditions while exhibiting a three times reduction in training time compared to all-electronic CNNs. This study underscores the potential of a hybrid optical-electronic neural network to enhance both performance and efficiency in OAM-based optical communication systems.
Developing energy-efficient optical components for computing is crucial for AI-driven hardware technologies. While previous studies primarily focused on optimizing cache-level design and managing write-intensive memory addresses, the impact of clock frequency on the energy consumption of emerging memory technologies, such as PCM, remains underexplored. In this work, through comprehensive simulation-based analysis, we reveal the complex relationship between clock frequency and the energy efficiency of OPCM, SRAM, and DRAM. The proposed memory architecture has demonstrated the potential to reduce overall energy consumption by up to 75% for the MiBench benchmark suite, a widely used set of embedded systems and IoT workloads. This work contributes to the ongoing efforts to improve the energy efficiency of optical computing systems, a critical factor in realizing the full potential of these emerging technologies.
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