The "Brute-Force" TaxWe are burning 5000W GPGPU clusters to run brute-force discrete simulations, just to "patch" the numerical gaps of Δt. This is the Discrete Cost: to get high-fidelity Sim-to-Real data, we compensate for low precision with massive parallelism. It’s an energetic dead-end.
The Breakthrough: Hypercomplex Causal LogicWe are introducing a New Computing Primitive based on Hypercomplex (Octonion) Manifolds.
Unlike traditional tensors, this state-space internalizes "Time-flow" in its real part and "Coupling-strength" in its imaginary parts.
Why this changes AI Inference (The "One-Look" Advantage):
Traditional NNs: Need to "see" 10+ frames of images to infer velocity and acceleration.
Our Paradigm: Because the state-space is inherently causal and coupled, a Transformer needs only one "look" (a single state) to understand motion trends.
Impact: This drastically shortens the Transformer sequence length, enabling ultra-low power inference on edge devices.
The Power Dividend: 5000W vs. 100W
5000W (Discrete): The cost of brute-force GPU clusters struggling to "patch" accuracy.
100W (Algebraic): A dedicated Causal Processor (FPGA/ASIC) running our Physics Algebraic Kernel. It bypasses discrete iterations entirely, delivering data-center-level fidelity at the edge.
The Hardware VisionThis isn't just software. We are positioning the Physics Algebraic Kernel as a "Co-processor." It runs on FPGA/ASIC to provide "Physical Intuition" for the adjacent AI chip (like NVIDIA Orin/Jetson), providing a higher-dimensional, continuous feature space that current neural networks crave.
The "Brute-Force" TaxWe are burning 5000W GPGPU clusters to run brute-force discrete simulations, just to "patch" the numerical gaps of Δt. This is the Discrete Cost: to get high-fidelity Sim-to-Real data, we compensate for low precision with massive parallelism. It’s an energetic dead-end.
The Breakthrough: Hypercomplex Causal LogicWe are introducing a New Computing Primitive based on Hypercomplex (Octonion) Manifolds.
Unlike traditional tensors, this state-space internalizes "Time-flow" in its real part and "Coupling-strength" in its imaginary parts.
Why this changes AI Inference (The "One-Look" Advantage):
Traditional NNs: Need to "see" 10+ frames of images to infer velocity and acceleration.
Our Paradigm: Because the state-space is inherently causal and coupled, a Transformer needs only one "look" (a single state) to understand motion trends.
Impact: This drastically shortens the Transformer sequence length, enabling ultra-low power inference on edge devices.
The Power Dividend: 5000W vs. 100W
5000W (Discrete): The cost of brute-force GPU clusters struggling to "patch" accuracy.
100W (Algebraic): A dedicated Causal Processor (FPGA/ASIC) running our Physics Algebraic Kernel. It bypasses discrete iterations entirely, delivering data-center-level fidelity at the edge.
The Hardware VisionThis isn't just software. We are positioning the Physics Algebraic Kernel as a "Co-processor." It runs on FPGA/ASIC to provide "Physical Intuition" for the adjacent AI chip (like NVIDIA Orin/Jetson), providing a higher-dimensional, continuous feature space that current neural networks crave.
Deep-Dive on NVIDIA Discussions:https://github.com/isaac-sim/IsaacSim/discussions/394#discus...