Scientists from the University of Pennsylvania claim to have designed a photonic chip which can recognize an image in under 0.57 nanoseconds (opens in new tab). The test chip was just 9.3mm square, and is said to be the first deep neural network implemented entirely on a scalable integrated photonic device.
It is worth emphasizing the sheer speed of image classification that the new photonics chip affords. If run continuously, the 0.57 nanosecond recognition time means the chip could classify a remarkable 1.75 billion images per second. In other words it is recognizing images at a rate of 1.75GHz.
Tom's Hardware reported on advances in optical chip and photonics technology on multiple occasions. This kind of technology is increasingly popular in super high-frequency applications where light-based components don't suffer from the resistance/heat problems that would affect traditional microelectronics with their wire interconnects. Thus we have seen photonic chip development examples cluster around solutions like high speed networking.
The scientists at the University of Pennsylvania are using both photonic technology and neural networks for their impressive image processing achievements. Traditionally, silicon chips like CPUs and GPUs have been used to process neural nets, and firms like Nvidia (opens in new tab) boast about the speeds at which their processors can run AI systems to recognize images (e.g. faces, objects), sounds, and video. However, the University of Pennsylvania scientists are the first to simulate neurons using an optical chip, with all the benefits this technology can deliver - such as very high speeds and low power consumption.
The research paper reveals that the scientists trained their optical neural net with letters of the alphabet and achieved a successful recognition rate for hand drawn characters of roughly 90%. This isn't the most complex of neural net AI tasks, which helped with the breathtaking speeds claimed. Moreover, the texts were limited to a 6x5 pixel grid, making things even simpler for the neural net to learn, and to accurately recognize.
With the scientists claiming scalability, it is reasonable to assume subsequent developments will make this photonic chip more useful in computer vision, 3D object classification, medical diagnosis, and other tasks. As for speed, the scientists say that they could up the recognition rate of the current chip to 0.1 nanoseconds using the best contemporary fabrication processes. That would mean the potential to classify 10 billion images per second, all else being equal.
Above we mentioned the use of neural nets to classify videos and 3D objects, and the Pennsylvania team intends to train their sub-1cm square photonic chips for recognition tasks with these inputs. Furthermore, they confirm that they will work on photonic chips with more pixels and neurons for classifying more complex and higher resolution images.