Analog photonic multiply-accumulate (MAC) processors are a promising alternative to digital electronics for energy-intensive workloads such as artificial intelligence. Prototypes include photonic crossbar arrays, Mach–Zehnder interferometer networks, and microring resonators. However, predicting large-scale performance remains challenging due to inconsistent benchmarking of system-level energy metrics. We address this gap by evaluating the energy budget and scaling behavior ofanalog photonic processors. Our framework establishes a unified benchmarking approach, enabling objective comparison across architectures and guiding the development of energy-efficient photonic hardware. Any analog photonic MAC processor integrated with conventional compute requires both electro-optical and digital-to-analog conversions, and vice versa. At scale, the per-MAC costs of digital and analog conversions decrease linearly with the matrix size, while costs of electro-optical conversions depend on architecture and associated propagation losses. We show that low-loss optical circuits are essential to surpass state-of-the-art electronic hardware in energy efficiency. Finally,we propose a scalable hybrid electro-optical MAC processor that reduces optical losses while operating with incoherent light, providing a path toward reprogrammable energy-efficient photonic accelerators tailored for AI workloads.
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