Light-carrying chips advance machine learning
International team of researchers uses photonic networks for pattern recognition
- January 6, 2021
- University of Münster
- Researchers found that so-called photonic processors, with which data is processed by means of light, can process information very much more rapidly and in parallel than electronic chips.
In the digital age, data traffic is growing at an exponential rate. The demands on computing power for applications in artificial intelligence such as pattern and speech recognition in particular, or for self-driving vehicles, often exceeds the capacities of conventional computer processors. Working together with an international team, researchers at the University of Münster are developing new approaches and process architectures which can cope with these tasks extremely efficient. They have now shown that so-called photonic processors, with which data is processed by means of light, can process information much more rapidly and in parallel -- something electronic chips are incapable of doing.
Background and methodology
Light-based processors for speeding up tasks in the field of machine learning enable complex mathematical tasks to be processed at enormously fast speeds (10¹² -10¹⁵ operations per second). Conventional chips such as graphic cards or specialized hardware like Google's TPU (Tensor Processing Unit) are based on electronic data transfer and are much slower. The team of researchers led by Prof. Wolfram Pernice from the Institute of Physics and the Center for Soft Nanoscience at the University of Münster implemented a hardware accelerator for so-called matrix multiplications, which represent the main processing load in the computation of neural networks. Neural networks are a series of algorithms which simulate the human brain. This is helpful, for example, for classifying objects in images and for speech recognition.
The researchers combined the photonic structures with phase-change materials (PCMs) as energy-efficient storage elements. PCMs are usually used with DVDs or BluRay discs in optical data storage. In the new processor this makes it possible to store and preserve the matrix elements without the need for an energy supply. To carry out matrix multiplications on multiple data sets in parallel, the Münster physicists used a chip-based frequency comb as a light source. A frequency comb provides a variety of optical wavelengths which are processed independently of one another in the same photonic chip. As a result, this enables highly parallel data processing by calculating on all wavelengths simultaneously -- also known as wavelength multiplexing. "Our study is the first one to apply frequency combs in the field of artificially neural networks," says Wolfram Pernice.
In the experiment the physicists used a so-called convolutional neural network for the recognition of handwritten numbers. These networks are a concept in the field of machine learning inspired by biological processes. They are used primarily in the processing of image or audio data, as they currently achieve the highest accuracies of classification. "The convolutional operation between input data and one or more filters -- which can be a highlighting of edges in a photo, for example -- can be transferred very well to our matrix architecture," explains Johannes Feldmann, the lead author of the study. "Exploiting light for signal transference enables the processor to perform parallel data processing through wavelength multiplexing, which leads to a higher computing density and many matrix multiplications being carried out in just one timestep. In contrast to traditional electronics, which usually work in the low GHz range, optical modulation speeds can be achieved with speeds up to the 50 to 100 GHz range." This means that the process permits data rates and computing densities, i.e. operations per area of processor, never previously attained.
The results have a wide range of applications. In the field of artificial intelligence, for example, more data can be processed simultaneously while saving energy. The use of larger neural networks allows more accurate, and hitherto unattainable, forecasts and more precise data analysis. For example, photonic processors support the evaluation of large quantities of data in medical diagnoses, for instance in high-resolution 3D data produced in special imaging methods. Further applications are in the fields of self-driving vehicles, which depend on fast, rapid evaluation of sensor data, and of IT infrastructures such as cloud computing which provide storage space, computing power or applications software.
- J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, J. Liu, C. D. Wright, A. Sebastian, T. J. Kippenberg, W. H. P. Pernice, H. Bhaskaran. Parallel convolutional processing using an integrated photonic tensor core. Nature, 2021; 589 (7840): 52 DOI: 10.1038/s41586-020-03070-1
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The older we grow, the weaker our muscles get, riddling old age with frailty and physical disability. But this doesn't only affect the individual, it also creates a significant burden on public healthcare. And yet, research efforts into the biological processes and biomarkers that define muscle aging have not yet defined the underlying causes.
Now, a team of scientists from lab of Johan Auwerx at EPFL's School of Life Sciences looked at the issue through a different angle: the similarities between muscle aging and degenerative muscle diseases. They have discovered protein aggregates that deposit in skeletal muscles during natural aging, and that blocking this can prevent the detrimental features of muscle aging. The study is published in Cell Reports.
"During age-associated muscle diseases, such as inclusion body myositis (IBM), our cells struggle to maintain correct protein folding, leading these misfolded proteins to precipitate and forming toxic protein aggregates within the muscles," explains Auwerx. "The most prominent component of these protein aggregates is beta-amyloid, just like in the amyloid plaques in the brains of patients with Alzheimer's disease."
In the study, the scientists identify amyloid-like protein aggregates in aged muscles from different species, from the nematode C. elegans all the way to humans. In addition, they also found that these aggregates also impair mitochondrial function. Although aggregated proteins have been suggested to contribute to brain aging, this is the first time that they have been shown to contribute to muscle aging and to directly damage mitochondria. "These abnormal proteotoxic aggregates could serve as novel biomarkers for the aging process, beyond the brain and muscle," says Auwerx.
But can the formation of the protein aggregates be reversed? To answer this, the researchers fed worms the vitamin nicotinamide riboside and the antitumor agent Olaparib, both of which boost the levels of nicotinamide adenine dinucleotide (NAD+), a biomolecule that is essential for maintaining mitochondrial function, and whose levels decline during aging.
In the worms, the two compounds turned on the defense systems of the mitochondria, even when provided at advanced age. Turning on the so-called "mitochondrial quality control system" reduced the age-related amyloid protein aggregates and improved the worms' fitness and lifespan.
The scientists then moved on to human muscle tissue, taken from aged subjects and IBM patients. Turning on the same mitochondrial quality control systems produced similar improvements in protein and mitochondrial homeostasis. The encouraging results led the researchers to test nicotinamide riboside in aged mice. The treatment also activated the mitochondrial defense systems and reduced the number and size of amyloid aggregates in different skeletal muscle tissues.
"Drugs that boost mitochondrial quality control could therefore be tested in the clinic to reverse these age-related proteotoxic aggregates and rejuvenate tissues," says Mario Romani, the first author of the study.
Gwangju Institute of Science and Technology
Chungnam National University School of Medicine
Mario Romani, Vincenzo Sorrentino, Chang-Myung Oh, Hao Li, Tanes Imamura de Lima, Hongbo Zhang, Minho Shong, Johan Auwerx. NAD+ boosting reduces age-associated amyloidosis and restores mitochondrial homeostasis in muscle. Cell Reports 19 January 2021. DOI: 10.1016/j.celrep.2020.108660