About the program

Publications

Project Description

Project Goal: Develop a novel neuromorphic hardware platform utilizing combined photonic and electronic spiking neurons for ultra-low energy and high-speed artificial intelligence applications.

Challenges Addressed:

  • Current AI models require increasingly high computational resources, leading to limitations in conventional digital hardware.
  • Existing spiking neural networks (SNNs) based on electronics face limitations in speed and energy efficiency.

Proposed Solution:

SPIKEPro will create a first-of-its-kind integrated circuit (IC) platform combining:

  • All-optical spiking neurons: Utilizing micron-sized lasers for ultrafast and energy-efficient spike generation.
  • Electronic spiking neurons: Employing resonant-tunneling diode (RTD) devices for low-energy electrical spiking.
  • Non-volatile synaptic weights: memristive devices offering fast switching and compatibility with the electronic-photonic integration platform.

Benefits:

  • Ultra-low energy consumption: Targeting sub picojoule range for photonic and picojoule range for electronic spiking events.
  • High-speed processing: Enabling operation at picosecond to nanosecond timescales.
  • Multimodal data processing: Handling both electrical and optical input signals.

Potentiaal Applications:

  • Energy-efficient AI systems for Internet-of-Things (IoT), smart buildings, and autonomous systems.
  • High-performance computing with SNN co-processing alongside digital neuromorphic hardware.

Expected Impact:

  • Transform AI systems into environmentally sustainable technologies.
  • Advance neuromorphic hardware with significant societal and economic benefits.

Overall, SPIKEPro presents a groundbreaking approach to ultra-low energy and high-speed SNN hardware, paving the way for a new era of efficient and powerful AI systems

Dr. Weiming Yao

Project Coordinator:T U/e Department of Electrical Engineering Photonic Integration Research Group