ReDON: Recurrent Diffractive Optical Neural Processor with Reconfigurable Self-Modulated Nonlinearity

TL;DR

The Recurrent Diffractive Optical Neural Processor (ReDON), a novel architecture featuring reconfigurable, recurrent self-modulated nonlinearity, establishes a new paradigm for reconfigurable nonlinear optical computing, uniting recurrence and self-modulation within non-von Neumann analog processors.

Abstract

Diffractive optical neural networks (DONNs) have demonstrated unparalleled energy efficiency and parallelism by processing information directly in the optical domain. However, their computational expressivity is constrained by static, passive diffractive phase masks that lack efficient nonlinear responses and reprogrammability. To address these limitations, we introduce the Recurrent Diffractive Optical Neural Processor (ReDON), a novel architecture featuring reconfigurable, recurrent self-modulated nonlinearity. This mechanism enables dynamic, input-dependent optical transmission through in-situ electro-optic self-modulation, providing a highly efficient and reprogrammable approach to optical computation. Inspired by the gated linear unit (GLU) used in large language models, ReDON senses a fraction of the propagating optical field and modulates its phase or intensity via a lightweight parametric function, enabling effective nonlinearity with minimal inference overhead. As a non-von Neumann architecture in which the primary weighting elements (metasurfaces) remain fixed, ReDON substantially extends the nonlinear representational capacity and task adaptability of conventional DONNs through recurrent optical hardware reuse and dynamically tunable nonlinearity. We systematically investigate various self-modulation configurations to characterize the trade-offs between hardware efficiency and computational expressivity. On image recognition and segmentation benchmarks, ReDON improves test accuracy and mean intersection-over-union (mIoU) by up to 20% compared with prior DONNs employing either optical or digital nonlinearities at comparable model complexity and negligible additional power consumption. This work establishes a new paradigm for reconfigurable nonlinear optical computing, uniting recurrence and self-modulation within non-von Neumann analog processors.

Figures (13)

• Figure 1: Inspired by GLU in LLMs, we propose a diffractive self-modulated nonlinear unit in ReDON. A small fraction of the light is sensed and self-modulates downstream metasurfaces.
• Figure 2: ReDON software--hardware correspondence. Top: software abstraction of a titlename block, where the optical operator $\mathcal{F}_{\text{titlename}}(\cdot)$ is integrated with lightweight pointwise layers and a compact digital head; recurrence reuses the same optical core with shared metasurface phases $\Phi$ and iteration-dependent modulation parameters $\Theta_r$. Bottom: a corresponding optoelectronic realization using an input encoder (e.g., LC-SLM), passive metasurface stack, intermediate optical sensing (coupling ratio $\alpha$), and a lightweight electronic processor that computes $\Psi(\cdot,\Theta)$ to drive a modulation plane for self-modulated nonlinearity.
• Figure 3: Compare different input encoding functions for ReDON on QuickDraw-10 classification. Phase and intensity encoding show the best effects.
• Figure 4: Evaluation of nonlinear expressivity by fitting popular activation functions. We simulate a $3\times 3$ phase-encoded input where only the center pixel sweeps phase $x\in[0,1]$ (others fixed at unit amplitude and zero phase) and read out only the center detector intensity to form a scalar mapping. ReDON (with a second-order polynomial $\Psi$ and residual readout $x-\eta\mathcal{F}_{\text{ReDON}}(x)$) approximates common nonlinear activations, while a linear DONN fails.
• Figure 5: (a) Group-wise and (b) layer-wise parameter sharing on metasurface modulation.