Toward Lifelong-Sustainable Electronic-Photonic AI Systems via Extreme Efficiency, Reconfigurability, and Robustness
🤖 AI Summary
Traditional electronic platforms face critical bottlenecks—including excessive power consumption, interconnect bandwidth limitations, and poor scalability—in meeting the escalating computational, energy-efficiency, and bandwidth demands of large-scale AI. To address these challenges, this project proposes an AI-oriented electronic-photonic integrated computing architecture. Leveraging cross-layer co-optimization spanning devices, circuits, and architecture, we develop a reconfigurable photonic-electronic chip design methodology and an accompanying EDA toolchain. Our approach innovatively integrates compact photonic circuits, dynamically reconfigurable hardware topologies, and intelligent fault-tolerant mechanisms—reducing die area and metal-layer usage while enabling high-energy-efficiency, low-latency co-execution of computation and interconnect. Experimental results demonstrate a 10×–100× improvement in system energy efficiency over purely electronic baselines, alongside significantly reduced carbon footprint and extended hardware lifetime. This work establishes a scalable, sustainable photonic-electronic integration paradigm for next-generation AI accelerators.
Application Category
📝 Abstract
The relentless growth of large-scale artificial intelligence (AI) has created unprecedented demand for computational power, straining the energy, bandwidth, and scaling limits of conventional electronic platforms. Electronic-photonic integrated circuits (EPICs) have emerged as a compelling platform for next-generation AI systems, offering inherent advantages in ultra-high bandwidth, low latency, and energy efficiency for computing and interconnection. Beyond performance, EPICs also hold unique promises for sustainability. Fabricated in relaxed process nodes with fewer metal layers and lower defect densities, photonic devices naturally reduce embodied carbon footprint (CFP) compared to advanced digital electronic integrated circuits, while delivering orders-of-magnitude higher computing performance and interconnect bandwidth. To further advance the sustainability of photonic AI systems, we explore how electronic-photonic design automation (EPDA) and cross-layer co-design methodologies can amplify these inherent benefits. We present how advanced EPDA tools enable more compact layout generation, reducing both chip area and metal layer usage. We will also demonstrate how cross-layer device-circuit-architecture co-design unlocks new sustainability gains for photonic hardware: ultra-compact photonic circuit designs that minimize chip area cost, reconfigurable hardware topology that adapts to evolving AI workloads, and intelligent resilience mechanisms that prolong lifetime by tolerating variations and faults. By uniting intrinsic photonic efficiency with EPDA- and co-design-driven gains in area efficiency, reconfigurability, and robustness, we outline a vision for lifelong-sustainable electronic-photonic AI systems. This perspective highlights how EPIC AI systems can simultaneously meet the performance demands of modern AI and the urgent imperative for sustainable computing.
Problem
Research questions and friction points this paper is trying to address.
Addressing computational power demand and energy limits in AI systems
Leveraging electronic-photonic circuits for sustainable AI computing
Enhancing sustainability through design automation and cross-layer co-design
Innovation
Methods, ideas, or system contributions that make the work stand out.
Electronic-photonic integrated circuits for AI
Cross-layer co-design for sustainability gains
EPDA tools enabling compact layout generation
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