SPIKING PHOTONIC-ELECTRONIC IC FOR QUICK AND EFFICIENT PROCESSING

Introduction

Rapid advances in artificial intelligence technologies have led to powerful models and algorithms that have revolutionized many applications across all fields of science and technology. Deep learning performed within artificial neural networks has yielded new ways to process data, leading to sophisticated systems with impressive functionality and benefits.

Need for New Computing Hardware

However, conventional computing hardware is reaching its limits in terms of energy efficiency and speed. A new approach to computing hardware is needed.

Neuromorphic Chips

Novel brain-inspired or neuromorphic chips working with biologically-inspired spiking neural networks have gained attention as they promise highly efficient ways to process data. Important research effort has been dedicated to developing such neuromorphic systems in electronic or photonic hardware separately, each with its drawbacks and limitations.

SPIKEPro's Innovation

SPIKEPro proposes a science-towards-technology breakthrough by combining low-energy electrical and photonic neurons into a joint spiking neural network on an integrated circuit. SPIKEPro’s chip integration approach is based on a common technology platform, connecting ultrafast laser optical neurons with efficient electrical spiking diodes through non-volatile synaptic weights.

Advantages of SPIKEPro

This enables the simultaneous capitalization on the advantages of both electronics and photonics to deliver efficient and high-speed SNNs going beyond existing implementations.

Energy Efficiency and Learning Strategies

In addition to reducing the energy consumption per spike in the network, SPIKEPro will also develop novel learning strategies and algorithms able to work with a reduced number of synaptic connections. These will be possible by exploiting the hardware parameters of the electrical and photonic spiking devices.

Impact of SPIKEPro

The outcome of SPIKEPro will have lasting economic, societal, and scientific impact. The project will bring ultra-fast and efficient neuromorphic hardware into the disparate fields of:

1. Edge computing
2. Sensor data processing
3. High-speed control
4. Computational neuroscience