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Optical processors for high performance and energy efficient computing for data centres and supercomputers

Moore’s Law which stated that the number of transistors on a chip will double approximately every two years has been the driver of semiconductor industry in boosting the complexity, computational performance and energy efficiency while reducing cost. The increase in computing performance of electronic computers is becoming more and more difficult, as dimensions approach nano meter ranges. The solution has been using parallel processing with multiple cores. While this has enhanced performance, it has led to the problem of rising energy consumption due to enhanced communications required between these cores and outside components. So much so that the communication on and between chips is now responsible for more than half of the total power consumption of the computer. With data increasing 70% year over year, analysts predict that in a few years, data centers may not have enough electricity to support all of the data storage and transfer.

 

One of the alternative researchers are considering is optical computing . An optical computer is a computer that performs its computation with photons as opposed to the more traditional electron-based computation.  An electric current creates heat in computer systems and as the processing speed increases, so does the amount of electricity required; this extra heat is extremely damaging to the hardware. Photons, however, create substantially less amounts of heat than electrons, on a given size scale, thus the development of more powerful processing systems becomes possible. Optical computers promise to be superfast since light travels at 186,000 miles per second. In a billionth of a second, one nanosecond, photons of light travel just a bit less than a foot, not considering resistance in air or of an optical fiber strand or thin film. The Electronic computers are relatively slow, and the faster we make them the more power they consume,the future optical computers shall  also be energy efficient.

 

The computers we use today use transistors and semiconductors to control electricity but computers of the future may utilize crystals and metamaterials to control light.  By applying some of the advantages of visible and/or IR networks at the device and component scale, a computer might someday be developed that can perform operations significantly faster than a conventional electronic computer.

 

The next generation computers and servers will replace electrical signals on copper lines with optical signals on waveguides. Optical lasers and photodiodes are  used to generate and receive the data signal.  The chips  using transmitters and receivers  can communicate with each other optically. Researchers are now trying  to use same principle for the communication on a chip–between cores and transistors. In future optics shall not only enhance inter and intra chip communications but also processing by Optical computers.

 

Optical computing is also promising for Brain like or neuromorphic computing, mimicking the human brain using electronic chips. And in turns out that optics are an excellent choice for this new brain-like way of computing. Research teams are  making breakthroughs  in the development of photonic computer chips that imitate the way the brain’s synapses operate. This would enable them to execute  current machine learning and deep neural learning algorithms with high speed and energy efficient way.

 

In March 2019,  Optalysys announced the FT:X 2000, the world’s first optical co-processor system for Ai computing. It is a really exciting time in optical computing,” said Dr. Nick New, Optalysys CEO and Founder. “As we approach the commercial launch of our main optical co-processor systems, we are seeing a surge in interest in optical methods, which are needed to provide the next level of processing capability across multiple industry sectors. We are on the verge of an optical computing revolution and it’s fantastic to be leading the way.”

 

 

The technology that is enabling optical processors and optical computers is silicon photonics. Silicon photonics refers to the application of photonic systems using silicon as an optical medium and yet have low manufacturing costs as a result of using conventional silicon-integrated-circuit processes. The silicon material used in such photonic systems is designed with sub micrometer precision and is deployed into the microphotonic components. Silicon photonics combines technologies such as complementary metal oxide semiconductor (CMOS), micro-electro-mechanical systems (MEMS) and 3D Stacking.

Optical Computers

There are two different types of optical computers, Electro-Optical Hybrid computers and Pure Optical computers.

Electro-Optical Hybrid computers

Most research projects focus on replacing current computer components with optical equivalents, resulting in an optical digital computer system processing binary data. Information gets sent in from keyboard, mouse, or other external sources and goes to the processor. Electro-Optical Hybrid computers use optical fibers and electric parts to read and direct data from the processor. Information is sent using Light pulses instead of voltage packets. Processor then sends the information through logic gates and switches to be programmed. Processors change from binary code to light pulses using lasers. Information is then detected and decoded electronically back into binary.

 

The information is then sent through different fiber optic cables depending on it’s final location. Some information will be sent to the holographic memory, where it will then be saved. After information is saved and the program would like to use it,the program sends a command to the processor, which then sends a command to receive the information.The program receives the information and sends a signal back to the processor to tell it that the task is complete.

 

This approach appears to offer the best short-term prospects for commercial optical computing, since optical components could be integrated into traditional computers to produce an optical/electronic hybrid. However, optoelectronic devices lose about 30% of their energy converting electrons into photons and back and this switching process slows down transmission of messages.

“Entirely optical computers are still some time in the future,” says Dr. Frazier, “but electro-optical hybrids have been possible since 1978, when it was learned that photons can respond to electrons through media such as lithium niobate. Newer advances have produced a variety of thin films and optical fibers that make optical interconnections and devices practical.

 

We are focusing on thin films made of organic molecules, which are more light sensitive than inorganics. Organics can perform functions such as switching, signal processing and frequency doubling using less power than inorganics. Inorganics such as silicon used with organic materials let us use both photons and electrons in current hybrid systems, which will eventually lead to all-optical computer systems. “What we are accomplishing in the lab today will result in development of super-fast, super-miniaturized, super-lightweight and lower cost optical computing and optical communication devices and systems,” Frazier explained.

 

Single-chip microprocessor that communicates directly using light developed by MIT in 2015

In 2015 Group of researchers at MIT, the University of California, Berkeley, and the University of Colorado, Boulder  developed an electronic-optical microprocessor by integrating over 70 million transistors and 850 optical components on a single chip working together to provide logic, memory, and interconnect functions, as described in the journal Nature.

 

The system uses on-chip photonic devices to directly communicate with other chips using light. “This is a milestone. It’s the first processor that can use light to communicate with the external world,” said Vladimir Stojanovic, a professor at the University of California, Berkeley, who led development of the chip. “No other processor has the photonic I/O in the chip.”

 

To integrate electronics and photonics at the scale of a microprocessor chip, we adopt a ‘zero-change’ approach to the integration of photonics. Instead of developing a custom process to enable the fabrication of photonics, which would complicate or eliminate the possibility of integration with state-of-the-art transistors at large scale and at high yield, we design optical devices using a standard microelectronics foundry process that is used for modern microprocessors.

 

The data transfers in the prototype occurred at a rate of 300 gigabits per second per square millimeter, which the researchers say is 10 to 50 times the rate for a comparable off-the-shelf electronic microprocessor. That boost in bandwidth could save a lot of energy in data centers, says Chen Sun, a researcher at the University of California, Berkeley.

 

He estimates that 20 to 30 percent of the energy used in data-center servers is spent transferring data between processor, memory, and networking cards. According to an analysis by the Natural Resources Defense Council, data centers in the United States will consume 140 billion kilowatt-hours of electricity a year by 2020, costing $13 billion and emitting 100 million metric tons of carbon. This demonstration could represent the beginning of an era of chip-scale electronic–photonic systems with the potential to transform computing system architectures, enabling more powerful computers, from network infrastructure to data centres and supercomputers.

 

In 2020 Lightmatter Introduced Optical Processor to Speed Compute for Next-Generation Artificial Intelligence

Since 2010, the amount of compute power needed to train a state-of-the-art AI algorithm has grown at five times the rate of Moore’s Law scaling—doubling approximately every three and a half months. Lightmatter’s processor solves the growing need for computation to support next-generation AI algorithms.

 

“The Department of Energy estimates that by 2030, computing and communications technology will consume more than 8 percent of the world’s power. Transistors, the workhorse of traditional processors, aren’t improving; they’re simply too hot. Building larger and larger datacenters is a dead end path along the road of computational progress,” said Nicholas Harris, PhD, founder and CEO at Lightmatter. “We need a new computing paradigm. Lightmatter’s optical processors are dramatically faster and more energy efficient than traditional processors. We’re simultaneously enabling the growth of computing and reducing its impact on our planet.”

 

In August 2020, Lightmatter’s VP of Engineering, Carl Ramey,  presented  their photonic processor architecture at HotChips32. The 3D-stacked chip package contains over a billion FinFET transistors, tens of thousands of photonic arithmetic units, and hundreds of record-setting data converters. Lightmatter’s photonic processor runs standard machine learning frameworks including PyTorch and TensorFlow, enabling state-of-the-art AI algorithms.

 

This new architecture is a massive advancement in the development of photonic processors. The performance of this photonic processor provides proof that Lightmatter’s approach to processor design delivers scalable speed and energy efficiency advantages over the current electronic compute paradigm and is the starting point for a roadmap of chips with dramatic performance improvements.

 

All Optical Processing

Pure Optical Computers  have no electron based systems or  require conversion from binary to optical thereby greatly increasing the speed.  They use multiple frequencies, and information is sent throughout computer as light waves and packets.  All-optical computers eliminate the need for switching. These computers will use multiple frequencies to send information throughout computer as light waves and packets thus not having any electron based systems and needing no conversation from electrical to optical, greatly increasing the speed.

 

All-optical signal processing has been considered a solution to overcome the bandwidth and speed limitations imposed by conventional electronic-based systems. Over the last few years, an impressive range of all-optical signal processors have been proposed, but few of them come with reconfigurability, a feature highly needed for practical signal processing applications. Following the electronic component design strategies, many equivalent photonic signal processors, such as temporal differentiators, temporal integrators, Hilbert transformers and ordinary differential equation (ODE) solvers, have been proposed and demonstrated.

 

Two very relevant examples of these fundamental devices are temporal differentiators and ODE solvers. Temporal differentiators can be used to perform real-time differentiation of an optical signal in the optical domain and have been applied to ultrafast signal generation and pulse characterizations. ODE solvers play an irreplaceable role in virtually any field of science or engineering, such as automatic control and temperature diffusion processes, writes Ming Li from Institute of Semiconductors, Chinese Academy of Sciences, No. 35, Tsinghua East Road, Beijing, 100083, China, and others.

 

Andrea Blanco-Redondo and Dr Chad Husko from CUDOS (ARC Centre of Excellence for Ultrahigh bandwidth Devices for Optical Systems) at the University of Sydney’s School of Physics have observed an on-chip soliton compression in a silicon photonic crystal for the first time. Solitons are nonlinear waves that propagate through a medium undistorted and their discovery shall enable development of nonlinear devices in silicon and all-optical processing systems.

 

UK-based startup Optalysys Rolls Commercial Optical Processor  and promises optical processors for supercomputers

Optalysys  completed 320 gigaFLOP optical computer prototype, targets 9 petaFLOP product in 2017 and 17 exaFLOPS machine by 2020.

 

Optalysys’ technology applies the principles of diffractive and Fourier optics to calculate the same processor intensive mathematical functions used in CFD (Computational Fluid Dynamics) and pattern recognition,” Dr. Nick New, CEO and founder of the company explained. “Using low power lasers and high resolution liquid crystal micro-displays, calculations are performed in parallel at the speed of light,” New further described.

The Optalysys Optical Solver Supercomputer reduces energy footprint so it can be considered as eco-efficient as well. No need for special power as it only needs a standard mains power supply. As for the running cost, the super processor only costs £2,100 every year. This is way cheaper than the fast supercomputer available in China, the Tianhe-2, developed by the National University of Defense Technology. The computer alone costs $320 million but with a $21m annual running cost.

 

Yorkshire-based Optalysys  in March 2019  announced commercial availability of its FT:X 2000 optical processor, which the company promotes as a post-Moore’s Law alternative. The low-power processor is aimed at computing-intensive AI workloads such as high-resolution image and video applications.

 

Optalysys said the FT:X 2000 can be programmed either through an API or a TensorFlow interface to perform optical correlation functions and convolutional neural network (CNN) implementations used for AI-based pattern recognition. The “entry-level system” operates at up to 2,400 frames per second with resolution measuring 2,048 by 1,536. “We are seeing a surge in interest in optical methods, which are needed to provide the next level of processing capability across multiple industry sectors,” said Optalysys CEO and founder Nick New.

 

 

 

 

References and Resources also include:

http://www.futureforall.org/computers/opticalcomputers.htm

http://www.southampton.ac.uk/news/2016/04/controlling-light-in-a-silicon-chip.page

http://www.ox.ac.uk/news/2017-09-28-scientists-create-brain-photonic-computer-microchips

https://techcrunch.com/2018/02/05/lightmatter-aims-to-reinvent-ai-specific-chips-with-photonic-computing-and-11m-in-funding/

https://www.hpcwire.com/2019/03/07/optalysys-rolls-commercial-optical-processor/

About Rajesh Uppal

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