A Photonic-Electronic non-von Neumann Processor Core for Highly Efficient Computing (APT-NuCOM)
A Photonic-Electronic non-von Neumann Processor Core for Highly Efficient Computing (APT-NuCOM)
批准号:
EP/W022931/1
负责人:
C Wright
金额:
$146.33万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
Modern society depends massively on the generation, processing and transmission of vast amounts of data. It is predicted that by 2025, 175 zettabytes (175 trillion gigabytes) of data will be generated around the globe, with so-called 'edge computing' devices creating more than 90 zettabytes alone. Processing such huge amounts of data demands ever increasing computational power, memory and communication bandwidth - demands that cannot be sustainably met by conventional digital electronic technologies. The growing gap between the needs and the capabilities of today's information technology is exemplified if we consider the historical trend in total number of computations (in units of #days of calculating at a rate of 1 PetaFLOP/s) needed to train various artificial intelligence (AI) systems. The trend followed Moore's Law (doubling approximately every two years) until 2012, after which the doubling time reduced to a mere 3.4 months! This trend is compounded by the breakdown in Koomey's Law, which states that the number of computations per Joule of energy doubles around every 1.5 years. This law was also followed until quite recently, but we are now approaching a widely accepted computing efficiency-wall at around 10 GMAC/Joule (a MAC is a multiply-accumulate operation) for CMOS electronics and the von-Neumann architecture. As a result, the energy consumption used in training modern AI systems is truly staggering, with consequent adverse effects for sustainability. This has led to a move away from standard CPU designs in AI towards the use of co-processors - GPUs, ASICs, FPGAs - with superior parallelism.However, even here the limitations of electrical signalling lead to massive levels of energy consumption. It was recently estimated, for example, that the training of a large GPU-based natural language processing system used for accurate machine translation resulted in carbon dioxide emissions equivalent to lifetime use of 5 cars! Clearly, a new approach is needed. Thus, in the APT-NuCOM project we will develop a highly efficient novel non-von Neumann co-processor that exploits clear advantages offered by photonic computation, but at the same time links seamlessly with the electronic domain to enable integration with existing electronic computing infrastructure. The APT-NuCOM co-processor will exploit novel phase-change photonic in-memory computing concepts to deliver massively parallel computation at PetaMAC/s speeds and, ultimately, an energy budget approaching that of the human brain.
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DOI:
10.1364/optica.485883
发表时间:
2023
期刊:
Optica
影响因子:
10.4
作者:
[Aggarwal S]
通讯作者:
Aggarwal S
Silicon Ring Resonator with Phase-Change Material as a Plastic Dynamical Node for Scalable All-Optical Neural Networks with Synaptic Plasticity
采用相变材料作为塑性动态节点的硅环谐振器,用于具有突触可塑性的可扩展全光神经网络
DOI:
10.1109/icton59386.2023.10207385
发表时间:
2023
期刊:
影响因子:
--
作者:
[Lugnan A]
通讯作者:
Lugnan A
DOI:
10.1364/cleo_si.2023.sf3e.8
发表时间:
2023-05
期刊:
2023 Conference on Lasers and Electro-Optics (CLEO)
影响因子:
--
作者:
[Frank Brückerhoff-Plückelmann;I. Bente;Daniel Wendland;J. Feldmann;C. D. Wright;H. Bhaskaran;W. Pernice]
通讯作者:
Frank Brückerhoff-Plückelmann;I. Bente;Daniel Wendland;J. Feldmann;C. D. Wright;H. Bhaskaran;W. Pernice
DOI:
10.1021/acsphotonics.3c00968
发表时间:
2023-10-18
期刊:
ACS PHOTONICS
影响因子:
7
作者:
[Lopez-Rodriguez, Bruno, van der Kolk, Roald, Aggarwal, Samarth, Sharma, Naresh, Li, Zizheng, van der Plaats, Daniel, Scholte, Thomas, Chang, Jin, Gro''blacher, Simon, Pereira, Silvania F., Bhaskaran, Harish, Zadeh, Iman Esmaeil]
通讯作者:
Zadeh, Iman Esmaeil
Optical switching dynamics of sub-micrometer Sb2Se3 thin films for active photonics
用于主动光子学的亚微米 Sb2Se3 薄膜的光学开关动力学
DOI:
10.1117/12.2682125
发表时间:
2023
期刊:
影响因子:
--
作者:
[Lawson D]
通讯作者:
Lawson D
New manufacturable approaches to the deposition and patterning of graphene materials
-
批准号:EP/K017160/1
-
项目类别:Research Grant
-
资助金额:$143.45万
-
财政年份:2013
-
负责人:C Wright
-
依托单位:
Materials World Network - Understanding and exploiting mixed-mode ultra-fast optical-electrical behavior in nanoscale phase change materials
-
批准号:EP/J018783/1
-
项目类别:Research Grant
-
资助金额:$46.72万
-
财政年份:2013
-
负责人:C Wright
-
依托单位:
海外基金