A Photonic-Electronic non-von Neumann Processor Core for Highly Efficient Computing (APT-NuCOM)
Lead Research Organisation:
UNIVERSITY OF EXETER
Department Name: Engineering
Abstract
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.
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.
Publications
Aggarwal S
(2024)
All optical tunable RF filter using elemental antimony
in Nanophotonics
Aggarwal S
(2023)
Reduced rank photonic computing accelerator
in Optica
Braid G
(2024)
Optical power-handling capabilities and temporal dynamics of reconfigurable phase-change metasurfaces.
in Optics express
Brückerhoff-Plückelmann F
(2023)
Hybrid Electro-Optic Crossbar Array for Matrix-Vector Multiplications
Brückerhoff-Plückelmann F
(2023)
Event-driven adaptive optical neural network.
in Science advances
Dong B
(2024)
Partial coherence enhances parallelized photonic computing
in Nature
Farmakidis N
(2024)
Integrated photonic neuromorphic computing: opportunities and challenges
in Nature Reviews Electrical Engineering
He Y
(2024)
Energy-Efficient Integrated Electro-Optic Memristors
in Nano Letters
Kendall S
(2025)
Dynamically reconfigurable 2D polarization-agnostic image edge-detection using nonvolatile phase-change metasurfaces
in Optics Express
| Description | We developed a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. We demonstrated a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically), along with a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy. We devised novel electro-optic memristor devices compatible with silicon photonics and capable of interfacing with both electrical and optical signals. We demonstrated ultra-low energy switching energy and a high electro-optical modulation efficiency, opening up opportunities for high-performance and energy-efficient integrated electro-optic neuromorphic computing. We demonstrated record endurance for the low-loss chalcogenide phase-change material Sb2Se3 (key to phase-change integrated photonic device applications) of 10 million cycles. Our work demonstrates that the combination of intrinsic film parameters with pumping conditions is particularly critical for achieving high endurance in optical phase change applications using Sb2Se3. We demonstrated a range of new computing and communications type applications for photonic systems based around optical metasurfaces incorporating Sb2Se3, including optical image processing, optical mode conversion and beam casting. |
| Exploitation Route | There are numerous applications for our work in terms of developing low-power AI-type processors, novel signal routing schemes for data server farms and ultra-fast optical image processing. |
| Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Electronics |
| Description | A number of patents by the investigators have been filed. |
| First Year Of Impact | 2024 |
| Sector | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Electronics |
| Impact Types | Economic |
