Projects
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| Reference Number | EP/Y020596/1 | |
| Title | Organic optoelectronic neural networks | |
| Status | Started | |
| Energy Categories | Not Energy Related 90%; Energy Efficiency (Industry) 10%; |
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| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Physics) 25%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor A Lvovsky Oxford Physics University of Oxford |
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| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 15 May 2024 | |
| End Date | 14 May 2027 | |
| Duration | 36 months | |
| Total Grant Value | £575,032 | |
| Industrial Sectors | Information Technologies | |
| Region | South East | |
| Programme | NC : ICT | |
| Investigators | Principal Investigator | Professor A Lvovsky , Oxford Physics, University of Oxford (100.000%) |
| Industrial Collaborator | Project Contact , Lumai Ltd (0.000%) |
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| Web Site | ||
| Objectives | ||
| Abstract | In recent years, machine intelligence (MI) based on artificial neural networks has made enormous progress, entering almost all spheres of technology, economy and our everyday life. However, much of the field's current growth is reliant on an ever-increasing consumption of computational power, and as a consequence electrical power. This growing demand for larger and faster systems is unsustainable, even with the current focus on developing bespoke hardware for MI processes. Today's data centres already consume about 2% of the total power generated worldwide. This number is growing exponentially; IBM vice president of research, Mukesh Khare, extrapolated in 2019 that the power consumed by neural networks could exceed the world's electricity production by 2040. We must therefore urgently look for fundamentally new computational principles to drive MI.A promising solution to this problem is to use light, rather than electrons, as the primary carrier of information in artificial neural networks. In optical neural networks (ONNs) the wave properties of light - coherence and superposition - can streamline the "matrix multiplication" operation (the most computationally expensive operation in MI), thereby offering a new route to greatly enhance computational speeds, with dramatically lower power consumption.This project aims to advance a crucial component of the ONN: the activation function (AF). This nonlinear function is applied to each neural unit as information passes through the multiple layers of a "feedforward" neural network, serving as a "gasket" between the layers of matrix multiplications. In principle, the AF role can be played by any nonlinear optical element. In practice, however, implementation of large ONNs with purely optical AFs is challenging due to losses, lack of flexibility and error accumulation.Here we will use organic semiconductor devices to provide the activation function, with circuits of photodiodes and OLEDs transforming and transferring the signal between optical layers. This will allow us to condition the signal at each layer and correct for possible errors, while still exploiting the advantages of light propagation for the computationally expensive steps. OLED displays in smart phones can contain millions of emitters, and so the concept is potentially scalable to very large ONNs capable of performing very complex computational tasks.The project is a collaboration of two groups. The PIs at the University of St Andrews are leaders in organic semiconductor optoelectronics and the Oxford PI possesses world-leading expertise in optical computing hardware. The St Andrews group will develop the "activation chips" - integrated arrays applying the AF to multiple optical units. The Oxford group will incorporate these activation chips into ONN systems suitable for various applications. In particular, a conceptually novel ONN system for computer vision will be developed. This system will allow a neural network to "see" and interpret objects directly, bypassing the need for converting an image into an electronic form. Such a system will have ultra-low latency and could find applications in autonomous vehicles, remote sensing and intelligent robotics. We will also use the activation chips to implement the Oxford group's innovative approach to direct training of ONNs, which does not involve digital simulation and hence is both faster and more robust to errors. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 05/06/24 | |
