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Photonic processor could streamline 6G wireless signal processing
- MIT researchers have developed a novel AI hardware accelerator for wireless signal processing, using an optical processor that performs machine-learning computations at the speed of light, classifying wireless signals in nanoseconds.
- The photonic chip is significantly faster and more energy-efficient than digital alternatives, making it scalable and flexible for various high-performance computing applications, including future 6G wireless technologies like cognitive radios.
- This new hardware accelerator enables edge devices to perform real-time deep-learning computations, potentially revolutionizing applications like autonomous vehicles' reactions to environmental changes or continuous monitoring by smart pacemakers.
- The optical neural network architecture, named MAFT-ONN, encodes signal data and conducts all machine-learning operations in the frequency domain, achieving high efficiency and scalability for signal processing.
- MAFT-ONN can fit 10,000 neurons on a single device, performing necessary multiplications efficiently through photoelectric multiplication and achieving signal classification with high accuracy in nanoseconds.
- The researchers aim to enhance MAFT-ONN further by implementing multiplexing schemes for increased computations, expanding into complex deep learning architectures, and optimizing performance for future applications.
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