Temporally plastic photonic processor for real-time adaptive computing

Area of Science:

• Photonics
• Hardware Acceleration
• Artificial Intelligence

Background:

• Real-time intelligent systems demand adaptive hardware, but current static-weight processors struggle with dynamic environments.
• Electronic hardware faces stability-plasticity trade-offs and memory/clocking bottlenecks for adaptive processing.
• Sequential processing in dynamic environments exacerbates limitations of conventional processors.

Purpose of the Study:

• To introduce a novel Temporally Plastic Photonic Processor (TPPP) for ultra-fast in situ adaptation.
• To overcome the limitations of electronic hardware in adaptive computing through a photonic approach.
• To demonstrate enhanced robustness and accuracy in sequential tasks using data-driven temporal plasticity.

Main Methods:

• Developed a TPPP integrating multi-timescale photonic kernels and recursive optical delay memory.
• Implemented a dual-kernel architecture: slow for stable processing, fast for transient adaptation.
• Embedded time-varying weights directly in the photonic domain, minimizing electronic memory access.

Main Results:

• Experimentally validated TPPP on linear and nonlinear sequential tasks.
• TPPP demonstrated superior robustness and accuracy compared to static photonic baselines.
• Projected up to 16x higher energy efficiency and 100x lower compute delay than electronic processors.

Conclusions:

• TPPP provides a promising hardware framework for real-time adaptive photonic computing.
• The photonic approach effectively addresses stability-plasticity trade-offs and memory bottlenecks.
• TPPP enables efficient and accurate processing in dynamic, evolving environments.