Grant: Research Tools and Instruments
Queen's University
$150,000.00
Real-time artificial intelligence with photonic computing
FY 2024-25
Summary
Neural networks have advanced artificial intelligence through machine learning. However, software implementations of neural networks on conventional computers, which have separate memory and processors and operate sequentially, are limited in both speed and energy efficiency. Neuromorphic engineering aims to create processors that mimic neurons and synapses in the brain, enabling distributed and parallel processing. When powered by photonics (optical physics), neuromorphic engineering offers sub-nanosecond latencies, high bandwidth, and low energy consumption. I am developing photonic neural networks (PNNs) that could solve foundational problems millions of times faster while consuming less energy than their electronic counterparts. These advancements could accelerate AI applications (such as the training of deep neural networks), solve combinatorial optimization problems (such as drug discovery and resource optimization for wireless networks), and enable real-time processing of radio signals for efficient satellite communications.
In the lab, PNNs are tested with a high-speed test and measurement setup that includes an arbitrary waveform generator (AWG) and a real-time oscilloscope (RTO), which is the focus of this grant request. RTOs are ideal for capturing high-frequency signals in real time, making them perfect for analyzing transient or non-repetitive events. The requested RTO will support various projects within my research program, funded by grants such as the Canada Research Chairs Program, the Ontario Early Researcher Award, and the recent launch of my NSERC CREATE-NUCLEUS-a pan-Canadian training program in photonic computing that bridges AI and quantum information. My research program also incorporates equity, diversity, inclusivity, anti-racism, and accessibility (EDIAA) principles, promoting the UN Sustainable Development Goals on Gender Equality, Reduced Inequalities, and Industry/Innovation. Additionally, it aligns with the Pan-Canadian AI Strategy, helping to pave the way for the next generation of computing technologies.
Program
Research Tools and Instruments
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RTI-2025-00660
Name nom
Shastri, Bhavin
Department département
Physics, Engineering Physics & Astronomy
Organization id
29
Institution établissement
Queen's University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2025
Award amount
150000
Program id
RTI
Program name en
Research Tools and Instruments
Program name fr
Outils et instruments de recherche
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1610
Committee name en
Electrical and Computer Engineering (RTI)
Committee name fr
Génie électrique et infomatique (OIR)
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2505
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Photon devices
Research subject fr
Dispositifs photoniques
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Real-time artificial intelligence with photonic computing
Keywords
photonic integrated circuits; thin-film lithium niobate photonics; photonic computing; neuromorphic photonics; nanophotonics
Application summary
Neural networks have advanced artificial intelligence through machine learning. However, software implementations of neural networks on conventional computers, which have separate memory and processors and operate sequentially, are limited in both speed and energy efficiency. Neuromorphic engineering aims to create processors that mimic neurons and synapses in the brain, enabling distributed and parallel processing. When powered by photonics (optical physics), neuromorphic engineering offers sub-nanosecond latencies, high bandwidth, and low energy consumption. I am developing photonic neural networks (PNNs) that could solve foundational problems millions of times faster while consuming less energy than their electronic counterparts. These advancements could accelerate AI applications (such as the training of deep neural networks), solve combinatorial optimization problems (such as drug discovery and resource optimization for wireless networks), and enable real-time processing of radio signals for efficient satellite communications.
In the lab, PNNs are tested with a high-speed test and measurement setup that includes an arbitrary waveform generator (AWG) and a real-time oscilloscope (RTO), which is the focus of this grant request. RTOs are ideal for capturing high-frequency signals in real time, making them perfect for analyzing transient or non-repetitive events. The requested RTO will support various projects within my research program, funded by grants such as the Canada Research Chairs Program, the Ontario Early Researcher Award, and the recent launch of my NSERC CREATE-NUCLEUS-a pan-Canadian training program in photonic computing that bridges AI and quantum information. My research program also incorporates equity, diversity, inclusivity, anti-racism, and accessibility (EDIAA) principles, promoting the UN Sustainable Development Goals on Gender Equality, Reduced Inequalities, and Industry/Innovation. Additionally, it aligns with the Pan-Canadian AI Strategy, helping to pave the way for the next generation of computing technologies.
