- Chinese photonic chips reportedly outperform conventional GPUs in narrow, specialized generative AI tasks
- ACCEL combines photonic and analog electronics to achieve high computing throughput
- LightGen uses over two million photonic neurons for all-optical generative AI processing
Chinese research institutes have described new photonic AI chips that would significantly outperform conventional GPUs under specific conditions.
Institutions and researchers say these chips show dramatic improvements in speed and power efficiency when running narrowly defined generative workloads.
They reported that Chinese light-based AI chips deliver 100x faster speed than Nvidia GPUs. for certain tasks, particularly in areas such as image synthesis, video generation, and vision-related inference.
Extreme speed claims from optical computing research
These claims are based on laboratory evaluations rather than commercial deployment scenarios, but the performance gap is closely related to fundamental architectural differences.
Nvidia GPUs, including widely used accelerators such as the A100, rely on electronic circuits in which electrons move through transistors to execute programmable instructions.
This approach allows flexibility across many workloads, but results in high power consumption, significant heat production, and reliance on advanced manufacturing nodes.
Chinese photonic chips instead rely on light-based signal processing, in which photons replace electrons as the computational medium, enabling massive parallelism through optical interference rather than sequential digital execution.
One of the reported chips, ACCEL, was developed at Tsinghua University as a hybrid system combining photonic components with analog electronic circuits.
It would operate using older semiconductor manufacturing processes while achieving extremely high theoretical throughput figures measured in petaflops.
These calculations are limited to predefined analog operations rather than the execution of general-purpose code.
ACCEL is therefore designed for tasks such as image recognition and vision processing.
These workloads rely on fixed mathematical transformations and tightly controlled memory access patterns.
A second system, LightGen, was developed through collaboration between Shanghai Jiao Tong University and Tsinghua University.
LightGen is described as an all-optical computer chip containing more than two million photonic neurons.
Research papers claim that it can perform generative tasks such as image generation, denoising, three-dimensional reconstruction and style transfer.
Experimental results would show performance gains exceeding two orders of magnitude compared to leading electronic accelerators.
These measurements are based on time and energy consumption under constrained conditions.
These systems are not described as replacing GPUs in general computing, training large models, or running arbitrary software.
They function as specialized analog machines designed for restricted categories of calculation.
The reported claims suggest that optical computing can generate exceptional gains when workloads are carefully designed to fit the hardware.
The gap between laboratory demonstrations and usable AI tools remains large, with task-specific versus general-purpose capabilities at the heart of evaluating these claims.
Via interesting engineering | China Daily | Eurek Alert
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