There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or even failure of Moore's law makes it increasingly difficult to improve their performance and energy efficiency by relying on advanced semiconductor technology. Optical devices can have a super-large bandwidth and low power consumption. And light has an ultrahigh-frequency of up to 100 THz and multiple degrees of freedom in their quantum state, making optical computing one of the most competitive candidates for high-capacity and low-latency matrix information processing in the “More than Moore” era. In recent years, photonic matrix multiplication has been developed rapidly and widely used in photonic acceleration fields such as optical signal processing, artificial intelligence, and photonic neural network.  These applications based on matrix multiplication show the great potential and opportunities in the photonic accelerator. 

In a new review published in Light Science & Application, a team of scientists, led by Professor Jianji Dong from Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology in China and co-workers have introduced the methods of photonic matrix multiplication and summarized the developmental milestones of photonic matrix multiplication and the related applications. Then, their detailed advances in applications to optical signal processing and artificial neural networks in recent years were reviewed. Comments on the challenges and perspectives of photonic matrix multiplication and photonic acceleration were also discussed

The paper reviewed and discussed the progress of photonic accelerators from a unique viewpoint of photonic matrix multiplication. These scientists summarize the main content of this review:

“The methods for photonic matrix-vector multiplications (MVMs) mainly fall into three categories: the plane light conversion (PLC) method, Mach–Zehnder interferometer (MZI) method, and wavelength division multiplexing (WDM) method.”

“The photonic matrix multiplication network itself can be used as a general linear photonic loop for photonic signal processing. In recent years, MVM has been developed as a powerful tool for a variety of photonic signal processing methods.”

“AI technology has been widely used in various electronics industries, such as for deep-learning-based speech recognition and image processing. MVM, as the basic building block of ANNs, occupies most of the computing tasks, such as over 80% for GoogleNet and OverFeat models. Improving the MVM performance is one of the most effective means for ANN acceleration. Compared with electrical computing, optical computing is poor at data storage and flow control, and the low efficiency of optical nonlinearities limits the applications in nonlinear computation, such as activation functions. While it has significant advantages on massively parallel supercomputing through multiplexing strategies of wavelength, mode, and polarization, extremely high data modulation speeds up to 100 GHz. Hence, photonic networks are quite good at MVM. The combination of optical computing and AI is expected to realize intelligent photonic processors and photonic accelerators. In recent years, AI technology has also seen rapid developments in the field of optics.”

“In general, photonic computing has obvious advantages in terms of signal rate, latency, power consumption, and computing density, and its accuracy is generally lower than that of electrical computing.”

“Before the all-optical ANNs are mature, especially in optical nonlinear effect and optical cascade, optoelectronic-hybrid AI is a more practical and more competitive candidate for deep ANNs. Therefore, the development of a highly efficient and dedicated optoelectronic-hybrid AI hardware chip system is one of the core research routes of photonic AI.”

Mathematicians at the University of Leicester have developed a new mathematical model which could greatly increase the efficiency of pest control and hence significantly reduce the impact of pests on crops whilst minimizing the damage to the environment. 

A new study builds upon individual-based model (IBM) techniques to explain and predict the formation of high slug density patches in arable fields.

While existing models built around the Turing theory of pattern formation (named for AI pioneer Alan Turing) and its generalizations are shown to work well for patterns in plant distribution, these are rarely able to accurately predict the distribution of animals due to the complexity of behavioral responses.

Drawing on field data collected in a three-year project, computational modeling experts in the University of Leicester’s School of Computing and Mathematical Sciences, alongside colleagues from The University of Birmingham and Harper Adams University, applied mathematical concepts to building a new model which shows trends of distribution, accounting for the movements of individual creatures.

Their model could be used in creating more efficient methods of pest control – by targeting the use of pesticides and other techniques to protect crops – and could be adapted to better understand the collective behavior in other species, such as fish schools, bird flocks, and insect swarms.

Sergei Petrovskii is a Professor in Applied Mathematics at the University of Leicester and the lead author for the study. Professor Petrovskii said: “This study is an example of how a fundamental ecological concept, when applied to a real-world problem, can lead to breakthrough findings and ultimately helps to make agriculture more sustainable”

Keith Walters, Professor in Agriculture and Pest Control at Harper Adams University, said: “Understanding factors determining slug distribution in agricultural fields have been a long-standing problem. Using unique field techniques specifically developed to support modeling and simulations allowed progress that would hardly be possible with empirical tools alone.”

Dr. Natalia Petrovskaya, Senior Lecturer in Applied Mathematics at the University of Birmingham and corresponding author for the study, added: “Computer simulations helped us to reveal a hidden link between biological processes going on very different spatial scales, which was crucial for the success of this project.”