Photonic Machine Learning for Time Series Problems
18 September 2025 10:00 - 11:00
Join us on an exciting webinar tutorial with Quantum Technology Lead, Prajnesh Vijay Kumar, on how photonic computing can overcome memory and power bottlenecks in modern AI workloads!
Join us on an exciting webinar tutorial with Quantum Technology Lead, Prajnesh Vijay Kumar, on how photonic computing can overcome memory and power bottlenecks in modern AI workloads! Modern AI workloads, especially for time series data, are increasingly bottlenecked by memory access and power consumption. Most of the energy in conventional hardware is spent shuttling data to and from memory—not actual computation. Photonic machine learning offers a paradigm shift by enabling analog, in-flight computation using light, drastically reducing both latency and power usage.
Event Description:
Modern AI workloads, especially for time series data, are increasingly bottlenecked by memory access and power consumption. Most of the energy in conventional hardware is spent shuttling data to and from memory—not actual computation. Photonic machine learning offers a paradigm shift by enabling analog, in-flight computation using light, drastically reducing both latency and power usage.
This tutorial introduces photonic reservoir computing as a promising approach, featuring two key platforms:
- EmuCore – An FPGA-based emulation platform to explore and prototype photonic-inspired reservoir architectures.
- NeuraWave – A full electro-optic PCIe system that performs real-time, high-throughput inference with ultra-low energy per operation.
We will present system-level concepts and benchmark results highlighting improvements in performance and power efficiency over traditional digital accelerators—demonstrating the potential of photonics for next-generation edge AI.
Sponsored By:
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Speaker
Prajnesh Vijay Kumar
QCi | Quantum Technology Lead
Dr. Prajnesh Kumar is a recognized expert in quantum optics, photonics, and AI-driven computational models, with deep experience in high-speed FPGA systems and optical computing. As the Quantum Technology Lead at Quantum Computing Inc., he spearheads research and development in photonic reservoir computing, quantum random walks, and real-time hardware acceleration for artificial intelligence.
Before joining Quantum Computing Inc., Dr. Kumar spent nearly a decade at Intel Corporation, where he served as a Systems and Hardware Engineer. There, he contributed to the development of advanced camera, touchscreen, and display subsystems for Intel’s next-generation platforms. His work involved complex SoC-based system design, embedded computing, and end-to-end hardware validation in collaboration with leading global OEMs.
Dr. Kumar earned his Ph.D. in Physics from Stevens Institute of Technology, where his doctoral research focused on electro-optic reservoir computing and high-speed optical systems. His interdisciplinary expertise spans embedded system design, photonic computing architectures, and FPGA-based AI accelerators—bridging theoretical quantum models with practical, scalable implementations.
He has authored numerous publications in high-impact journals, including Optics Letters and OSA Continuum, and has contributed to pioneering experiments in spatial optical computing and neuromorphic AI. In addition to his role at QCi, Dr. Kumar serves as a Visiting Scientist at the Clinical Neuroinformatics and AI Laboratory (CNAIL), where he explores the convergence of photonic computing and AI for advancing computational neuroscience.
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