<div id="readability-page-1">Chinese scientists have unveiled an optical computing chip that outperformed Nvidia’s leading AI hardware by over a hundredfold in speed and energy efficiency – particularly for generative tasks such as video production and image synthesis.\nThe LightGen chip was developed by a team from Shanghai Jiao Tong University and Tsinghua University, harnessing the speed of light to execute complex artificial intelligence workloads.\nWith more than 2 million photonic neurons integrated into a compact chip, LightGen can generate high-resolution images, including 3D scenes, and create videos.\nThe research, led by Professor Chen Yitong from Shanghai Jiao Tong University, was published in the journal Science on Friday.\nChen said LightGen could be “further scaled up” and added: “It provides a new way to bridge the new chip architectures to daily complicated AI without impairment of performance and with speed and efficiency that are orders of magnitude greater, for sustainable AI.”\nWith artificial intelligence advancing rapidly, generative AI can now produce realistic images and even videos – but it needs immense computing capacity and consumes large amounts of energy.\nAs a result, scientists have turned to photonic computing as conventional electronic chips reach their limits.\nTraditional computers rely on the flow of electrons to send and process information, while photonic computing uses laser pulses instead of electrons, performing operations at the speed of light.\nOptical signals also have the advantage of minimising power consumption and offering rapid responses to user requests.\nHowever, although photonic computing systems have shown potential in specific tasks, they previously struggled to handle high-complexity generative AI tasks – such as synthesising images and generating videos – because of limitations in their computing architecture and underdeveloped training algorithms.\nThe LightGen team’s work focused on developing three areas: building a new architecture, developing a novel training algorithm and giving the chip high integration density.\n\nArchitecturally, the team created an “optical latent space” – similar to an expandable “highway hub” for light – where data can flow rapidly in its most compact form, allowing for the efficient compression and reconstruction of information, according to the study.\nThe researchers also developed a generative training algorithm that, compared with conventional versions, removed the need for massive labelled data sets.\nInstead, they used an unsupervised training algorithm that allowed LightGen to learn and create by discerning statistical patterns in data along similar lines to the human learning process.\nThe team packed more than 2 million photonic “neurons” onto a chip of 136.5 sq mm (0.2 square inches), constructing a sophisticated network capable of handling high-resolution image generation.\nExperiments highlighted some of LightGen’s abilities, including the generation of animal images at 512×512 pixel resolution with diverse categories, colours, expressions and backgrounds, which were rich in detail and logically correct.\nThe study said: “LightGen experimentally implemented high-resolution semantic image generation, denoising [making grainy images appear cleaner and sharper], style transfer, three-dimensional generation and manipulation.”\nAt a conservative estimate, LightGen achieved a system computing speed of 3.57×10⁴ Tera Operations Per Second (TOPS) and an energy efficiency of 6.64×10² TOPS/watt.\nThis meant its overall performance surpassed that of leading electronic chips, such as Nvidia’s market-leading A100, by more than a hundredfold.\n“The improvement in computing speed and energy efficiency of LightGen corresponded well with the experimentally measured end-to-end reduction in time and energy cost when LightGen experimentally achieved generation quality comparable with that of real-world electronic AI models on Nvidia A100,” the paper said.\nThe researchers said LightGen could mark a significant shift in the hardware used for generative AI by making photonic computing a core platform capable of independently executing complex creative tasks.\nThey added that its extraordinary energy efficiency also offered a practical pathway to alleviate the growing energy demands of AI computing.\n</div>

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<p>Chinese scientists from Shanghai Jiao Tong University and Tsinghua University have developed an optical computing chip, LightGen, which outperforms Nvidia's leading AI hardware by over a hundredfold in speed and energy efficiency. The chip, integrating over 2 million photonic neurons, excels in generative tasks like video production and image synthesis. Published in Science, the research highlights LightGen's potential to revolutionize AI hardware with its high-speed, energy-efficient capabilities, marking a shift towards photonic computing for complex AI tasks.</p>

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Chinese team builds optical chip AI that is 100 times faster than Nvidia’s market leader