Artificial intelligence (AI) is rapidly transforming a wide range of industries. Powered by deep learning and vast datasets, AI systems require enormous computing power to train and operate. Today, most of this work relies on graphical processing units (GPUs), but their high energy consumption and limited scalability pose significant challenges. To support future growth in AI, more efficient and sustainable hardware solutions are needed.
A recent study published in the IEEE Journal of Selected Topics in Quantum Electronics introduces a promising alternative: an AI acceleration platform built on photonic integrated circuits (PICs). These optical chips offer better scalability and energy efficiency than traditional, GPU-based systems. Led by Dr. Bassem Tossoun, Senior Research Scientist at Hewlett Packard Labs, the research shows how PICs that incorporate III-V compound semiconductors can run AI workloads faster and with far less energy.
Unlike conventional hardware, which uses electronic distributed neural networks (DNNs), this new approach uses optical neural networks (ONNs), circuits that compute with light instead of electricity. Because they operate at the speed of light and minimize energy loss, ONNs hold great potential for accelerating AI more efficiently.
To read more, click here.