Chinese researchers have developed a new all-optical interconnect system linking standard electronic chips, boosting AI distributed inference speeds by over 100 times while using just one-ninth of the typical computational resources.
AI models are permeating ever more applications, expanding the industry’s appetite for computational power. The conventional response has been to pile on more GPUs and build ever-larger data centres in a seemingly endless race for energy and brute force.
But a new study from Peking University suggests a radically different path: by optically linking chips with specific algorithms, they boost inference speeds by a factor of over 100 while slashing compute needs to just one-ninth.
The work was published in the journal National Science Review, and its corresponding authors include Shu Haowen and Wang Xingjun from Peking University.
The team’s “Lego” building blocks were field-programmable gate array (FPGA) chips: programmable devices widely used in fields that demand high parallel-processing ability, such as missile guidance, autonomous driving and data centres.
The “joints” connecting these FPGAs are two custom-designed communication hardware components. The first is a silicon photonic transceiver chip running at 400 gigabits per second, responsible for converting electrical signals to optical and vice versa.