Rather than that number being converted into light multiple times—consuming energy each time—it can be transformed just once, and the light beam that is created can be split into many channels. In this way, the energy cost of input conversion is amortized over many operations. Splitting one beam into many channels requires nothing more complicated than a lens, but lenses can be tricky to put onto a chip. So the device we are developing to perform neural-network calculations optically may well end up being a hybrid that combines highly integrated photonic chips with separate optical elements.
I've outlined here the strategy my colleagues and I have been pursuing, but there are other ways to skin an optical cat. Another promising scheme is based on something called a Mach-Zehnder interferometer, which combines two beam splitters and two fully reflecting mirrors.
It, too, can be used to carry out matrix multiplication optically. Two MIT-based startups, Lightmatter and Lightelligence , are developing optical neural-network accelerators based on this approach. Lightmatter has already built a prototype that uses an optical chip it has fabricated. And the company expects to begin selling an optical accelerator board that uses that chip later this year. Another startup using optics for computing is Optalysis , which hopes to revive a rather old concept.
One of the first uses of optical computing back in the s was for the processing of synthetic-aperture radar data.
A key part of the challenge was to apply to the measured data a mathematical operation called the Fourier transform. Digital computers of the time struggled with such things. Even now, applying the Fourier transform to large amounts of data can be computationally intensive. But a Fourier transform can be carried out optically with nothing more complicated than a lens, which for some years was how engineers processed synthetic-aperture data.
Optalysis hopes to bring this approach up to date and apply it more widely. There is also a company called Luminous , spun out of Princeton University , which is working to create spiking neural networks based on something it calls a laser neuron. Spiking neural networks more closely mimic how biological neural networks work and, like our own brains, are able to compute using very little energy.
Luminous's hardware is still in the early phase of development, but the promise of combining two energy-saving approaches—spiking and optics—is quite exciting. There are, of course, still many technical challenges to be overcome. One is to improve the accuracy and dynamic range of the analog optical calculations, which are nowhere near as good as what can be achieved with digital electronics. That's because these optical processors suffer from various sources of noise and because the digital-to-analog and analog-to-digital converters used to get the data in and out are of limited accuracy.
Indeed, it's difficult to imagine an optical neural network operating with more than 8 to 10 bits of precision. While 8-bit electronic deep-learning hardware exists the Google TPU is a good example , this industry demands higher precision, especially for neural-network training. There is also the difficulty integrating optical components onto a chip. Because those components are tens of micrometers in size, they can't be packed nearly as tightly as transistors, so the required chip area adds up quickly.
A demonstration of this approach by MIT researchers involved a chip that was 1. Even the biggest chips are no larger than several square centimeters, which places limits on the sizes of matrices that can be processed in parallel this way.
There are many additional questions on the computer-architecture side that photonics researchers tend to sweep under the rug. What's clear though is that, at least theoretically, photonics has the potential to accelerate deep learning by several orders of magnitude.
Based on the technology that's currently available for the various components optical modulators, detectors, amplifiers, analog-to-digital converters , it's reasonable to think that the energy efficiency of neural-network calculations could be made 1, times better than today's electronic processors. Making more aggressive assumptions about emerging optical technology, that factor might be as large as a million. And because electronic processors are power-limited, these improvements in energy efficiency will likely translate into corresponding improvements in speed.
Many of the concepts in analog optical computing are decades old. Some even predate silicon computers. Schemes for optical matrix multiplication, and even for optical neural networks , were first demonstrated in the s. But this approach didn't catch on. Will this time be different?
Possibly, for three reasons. First, deep learning is genuinely useful now, not just an academic curiosity. Second, we can't rely on Moore's Law alone to continue improving electronics. And finally, we have a new technology that was not available to earlier generations: integrated photonics.
These factors suggest that optical neural networks will arrive for real this time—and the future of such computations may indeed be photonic.
Explore by topic. The Magazine The Institute. Device to device communication is an advanced data transmission technology developed to increase the efficiency of network. In LTE -Direct, D2D communication enabled devices can interact each other using a secure transmission protocol similar to the devices communicate with the base station. In order to have a stable communication and uninterrupted service, mobile devices must be within the proximity of base station.
Due to higher interference from surroundings and sometimes due to signal blocking physical structure like concrete walls or locations like basement, received signal strength would be lower than required.
Device to device communication technology is a best solution to improve the signal proximity scenario. If any device is connected to the mobile network, it can act as a relay station to other device s not under direct proximity of base station to establish a connection to the core network. Device to device communication can be achieved in multiple modes of operation depends on the scenarios.
According to the situation, most suitable operation mode will be chosen to establish efficient transmission. Scenario 1 : If two devices are in proximity they can start communication like sharing data. This helps to improve data rate, reduce power consumption of devices and total load reduction of base stations. The control will be handled by base station. Scenario 2 : During the absence of an active mobile network connection or insufficient signal reception, D2D enabled devices can establish an alternative communication interface with its surrounding devices which are connected mobile base stations.
Within this system, we propose a communication mechanism to aid traditional cellular communication and correspondingly borrow some channel resource from traditional cellular communication system for D2D communication. On one side, to aid cellular communication, we propose a modified Alamouti scheme which does not modify the operation at the base station. This makes our proposed scheme consistent with previous cellular communication system.
On the other side, there are many competitive D2D groups that want to potentially utilize the borrowed channel resource from traditional cellular system for delivering their own information. We model this competition as a game and utilize game theory technique to solve this competition problem. In traditional cellular systems, data is transmitted from the source terminal to the destination terminal via a base station BS.
All traffic is forwarded or relayed by the BS even if the source and the destination are close to each other. This relaying-structure has two drawbacks.
First, it incurs long communication latency and high energy consumption. Second, traditional BS can only support limited number of mobiles communicating simultaneously, which does not suit the exponential growth of mobile terminals, especially machine-to-machine M2M communications which are mainly adopted to collect signals from sensors and then delivered to the Internet [ 1 — 3 ].
To tackle these two problems, device-to-device D2D communication instead of communication via a relay was proposed as a short-distance communication option [ 4 — 7 ]. Firstly, it reduces communication latency and energy consumption due to shorter communication range and fewer transmitters. Secondly, it enlarges system capacity since shorter communication range of D2D communication can be viewed as a smaller cell utilizes the spectrum with a higher utilization rate per area and hence has higher system capacity.
There are a lot of application scenarios for D2D communication. Due to D2D's advantages as listed above, how to embed it into current cellular system needs to be carefully designed. In particular, radio resource e. There are two allocation modes. In the first, these two systems share the radio resource simultaneously, which introduces the interference between the D2D link and cellular link. In the second, these two systems use orthogonal channels.
Most of previous works for D2D communication assumed that D2D users worked in the underlay mode, namely, Mode One [ 8 — 11 ]. A radio resource allocation policy for D2D link is proposed in [ 12 ]. The work in [ 13 ] limits D2D's transmission power to ensure the quality of cellular links. It is also shown in [ 14 ] that power control in relay assisted D2D communication may be a better choice, which benefits from high transmission capacity and power efficiency.
To summarize, all those D2D works do not consider the cooperation between D2D groups with cellular system. In this work, we let the transmitter of the D2D group help the cellular communication, namely, help the source to relay his signal to the destination, which provides another link.
The two signals from these two links one from BS and the other from the D2D group can be combined in a certain manner, for example, maximum ratio combining MRC , to enhance the received signal-to-noise ratio SNR , which reduces the outage probability and increases the achieved diversity [ 15 ].
To implement the above, conventional diversity technique, Alamouti scheme is not compatible, since BS needs to perform conjugation on one of the transmission signals during the collaboration process. We propose a modified Alamouti scheme which does not modify the operation at BS; namely, the operation at the BS is identical to that in traditional cellular system.
This kind of cooperation can reduce the delivery time required for cellular communication system and hence the saved time can be rewarded to D2D communication.
During the communication process, free riding, however, exists in the D2D terminal. In this paper, the game theoretical framework [ 16 , 17 ] can solve this problem, and multi-D2D groups power can be controlled by Nash Equilibria [ 18 ]. This scheme can be utilized into high communication efficiency data collection in 5G mobile communication system [ 19 — 22 ], for example. The organization of this paper is as follows. In Section 2 , we first provide preliminaries and then describe the system model.
In Section 3 , we elaborate the proposed cooperation scheme that incorporates traditional cellular communication and D2D communication.
In Section 4 , we use game theoretical framework to design the competition among D2D groups. Finally, in Section 5 , we draw the conclusions. We first describe the decode-and-forward DF relaying protocol and then illustrate the Alamouti scheme. Refer to Figure 1. Transmitter sends signal toward receiver via relay. The link gain between and is , and the link gain between and is. The channel's input-output relationship of the two hops is characterized by where and denote the output signal and input signal of relay , respectively.
In the DF protocol, the relay node first tries to decode the message from the received signal. If the decoding is successful, the relay reencodes the message using the same codebook as in source.
Otherwise, the relay simply keeps silent. Refer to Figure 2. Transmitters 1 and 2 send signals simultaneously toward destination. The link gain between transmitter and destination is. The channel's input-output relationship is characterized as where denotes the transmitted signal by transmitter , denotes the received signal at destination , and denotes the thermal noise at destination , all at time slot. In the Alamouti scheme [ 23 ], the two transmitters intend to send two complex symbols and , respectively, over two consecutive symbol slots.
During slot 1, transmitter 1 sends and transmitter 2 sends ; namely, ,. During slot 2, transmitter 1 sends and transmitter 2 sends ; namely, ,. If we assume that the channel gain remains constant over these two consecutive symbol times, namely, , , then we have two consecutive received signals as.
Rewriting it into matrix form and performing the conjugation operation on the lower half matrix, we obtain. We observe that the two columns of the square matrix in 4 are orthogonal.
Hence, the detection problem for , decomposes into two separate, orthogonal, scalar problems. Throughout this paper, we use to denote the conjugate of the complex number and to denote the conjugate transpose of the complex matrix. Refer to Figure 3. There are two subsystems. One is the traditional cellular system, and the other is the D2D system. Node communicates with node via as a relay, which composes the traditional cellular system.
The users that are in a small circle form the D2D communication group due to their closeness to each other; namely, the communication within this group is one hop. The D2D group may help the source relay its transmitted signal to the destination, which provides another branch of link. The two signals arriving at the destination from these two links can be combined in a certain manner, for example, maximum ratio combining MRC to enhance the received SNR.
According to Shannon's capacity formula, the achieved rate is correspondingly increased, which brings the following benefits. The completion time of the communication link can be reduced and the reduced time reduction of time may be rewarded to the D2D users for communication.
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