Inside the Multi-Physics Fabric
A unified substrate that ends the von Neumann bottleneck by computing with light, events, and quantum coherence.
Unified Photonic Fabric
The Multi-Physics Photonic Integrated Circuit (MP-PIC) is a silicon-photonic substrate that unifies three distinct compute modes on a single chip.
Photonic waveguides carry data at light speed. Mach-Zehnder interferometers (MZIs) perform analog matrix operations. Optical ring resonators enable neuromorphic spiking. Gaussian Boson Sampling circuits solve quantum optimization problems.
All modes share the same physical layer—no data leaves the chip.
MP-PIC 3D Diagram
3D-Stacked Architecture
Multi-Physics Photonic Integrated Circuit
The Multi-Physics Compiler
Automatic workload partitioning across photonic, neuromorphic, and quantum modes.
Multi-Physics Compiler Workflow
Multi-Physics Compiler: PyTorch → Automatic Partitioning → Photonic/Neuromorphic/Quantum Execution
1. Graph Analysis
Parse PyTorch, JAX, or TensorFlow computation graphs and identify optimal operations for each mode.
2. Automatic Partitioning
Dense matrix ops → PhotonCore. Sparse events → NeuroMesh. Optimization → Q-Bridge.
3. Runtime Execution
Deploy seamlessly to Oenerga hardware. No code changes required.
Python API Example
import oenerga
import torch
# Load model and move to Oenerga device
model = MyModel().to("oenerga:0")
# Create input tensor on Oenerga device
x = torch.randn(1, 128, device="oenerga:0")
# Execute on multi-physics fabric
# Compiler automatically partitions across modes
y = model(x)
print(f"Output: {y.shape}")
# Transparent PyTorch integrationPhotonCore™ — Analog Optical Matrix Compute
PhotonCore executes matrix multiplications in the analog optical domain using Mach-Zehnder interferometer (MZI) meshes. Each MZI acts as a programmable weight, and wavelength-division multiplexing (WDM) enables massive parallelism.
- •128×128 MZI mesh: Thousands of parallel MAC operations per waveguide
- •10 ns latency: Light-speed compute, no electronic bottleneck
- •10 pJ/operation: 45× more efficient than GPU clusters (representative)
- •Use cases: Transformer inference, GEMM-heavy workloads, real-time AI
NeuroMesh™ — Optical Spiking Neural Networks
NeuroMesh implements spiking neural networks (SNNs) using optical ring resonators. Events propagate as photon spikes—zero power consumption at rest, sub-nanosecond response times.
- •Zero idle power: Only active neurons consume energy
- •120 ps response: Picosecond-scale event processing
- •Event-driven: Sparse activation, perfect for sensor fusion
- •Use cases: Autonomous vehicles, robotics, real-time video processing, telecom routing
Q-Bridge™ — Photonic Quantum Optimization
Q-Bridge leverages Gaussian Boson Sampling (GBS) to solve Quadratic Unconstrained Binary Optimization (QUBO) problems. GBS uses photon interference in a programmable photonic circuit to explore exponentially large solution spaces.
- •32-mode GBS: Explore 2³² state spaces in parallel
- •1 MHz sampling: Millions of samples per second
- •Production-ready: Available today, not a research prototype
- •Use cases: Portfolio optimization, molecular discovery, logistics, scheduling
Enterprise Deployment
Kubernetes operator with native resource scheduling
apiVersion: v1
kind: Pod
metadata:
name: oenerga-inference
spec:
containers:
- name: inference
image: my-app:latest
resources:
limits:
oenerga.com/mppic: 1
memory: "8Gi"
requests:
oenerga.com/mppic: 1
memory: "4Gi"Deploy with standard Kubernetes tools. Oenerga resources schedule like GPUs—no vendor lock-in.
Schedule a Technical BriefingReady to Deploy?
Download our technical whitepaper or schedule an enterprise briefing with our engineering team.