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System on Electronic Chip

[Category : - ELECTRONICS- SOFTWARES- DESIGN PATENTS]
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ControlAI-System on Electronic Chip designed by Sagacious Research and Development Labs in Canada is a breakthrough semiconductor design that learns, predicts, and controls physical systems directly in hardware. It merges Physics-Informed Neural Networks (PINN), Model Predictive Control (MPC), and Reinforcement Learning (RL) into a single self-learning chip—ready for fabrication (SkyWater 130 nm ? GlobalFoundries 28 nm).
It reduces energy use by 70 %, boosts inference speed > 3×, and enables fully autonomous control for smart grids, UAVs, satellites, and edge devices.

With our INVENTION ,top high-tech Companies can get more profits by adopting our INVENTION "ControlAI-SoC — Physics-Informed Intelligent System-on-Chip"

Tesla (US $0.5 billion) by enhancing vehicle autonomy and grid-integration efficiency

Google (Alphabet) (US $1 billion) improving TPU edge units for robotics and energy control;

Microsoft (around US $0.9 billion) adding ControlAI hardware into Azure IoT and edge nodes;

NVIDIA (US $4–6 billion) by adding real-time control and physics-based learning to Jetson / Orin edge platforms;

Boeing (US $0.4 billion) adding adaptive flight-control hardware for next-gen aircraft;

AMD (US $2–3 billion) through hybrid AI-control chips for IoT and automotive markets;

Intel (US $1–2 billion) via adaptive-control IP for AI-Edge and foundry solutions;

Qualcomm (about US $0.8 billion) from deterministic AI in drones and autonomous vehicles;

Apple (US $0.5 billion) by integrating physics-based AI into wearables and EV systems;

Amazon (AWS) (saves about US $0.8 billion per year) by reducing edge-compute energy costs through on-chip intelligence;

Broadcom (US $0.6 billion) with custom SoC IP for OEMs featuring embedded learning logic;

Lockheed Martin (US $0.6 billion) improving UAV autonomy and satellite energy resilience;

Raytheon (US $0.4 billion) integrating AI-based thermal control into defense electronics;

GE (US $0.7 billion) through smart-turbine and grid-control SoCs;

Siemens (US $0.6 billion) via AI-driven industrial controllers;

Schneider Electric (US $0.4 billion) with adaptive power-grid and automation chips;

Honeywell (US $0.3 billion) advancing self-learning aerospace and factory systems;

ABB (US $0.5 billion) improving renewable-grid stability through physics-informed models;

Emerson (US $0.3 billion) developing industrial IoT controllers with built-in AI feedback;

Palantir (US $0.3 billion) expanding into AI hardware analytics for defence and energy sectors.

Technical Edge

Energy & Latency: 70 % lower power, 10 ms control loop.

Reliability: On-chip self-correction and fault isolation.

Scalability: From 130 nm open source to 28 nm commercial node.

Domains: Smart grids | Aerospace | Autonomous vehicles | Defence systems

Why These Companies and How They Improve Their Technologies with This Invention

Many of these firms already push AI compute (NVIDIA, AMD, Google, Microsoft) but mostly in inference/training for general-purpose tasks. ControlAI-SoC gives them embedded control + physics-informed learning (not just generic inference) and opens new revenue domains (edge, aerospace, defence, energy).

Industrial & energy companies (GE, Siemens, Schneider, ABB) often use standard controllers and separate AI-software layers. With this invention they can embed the AI/ML + control logic together in one SoC, reducing latency, improving reliability, lowering cost, accelerating product development.

Aerospace & defence primes (Lockheed Martin, Raytheon, Boeing) can integrate this silicon into UAVs, satellites, guided systems, smart grids for base-defence infrastructures, thereby improving autonomy, reducing SW complexity, lowering SW certification burden (since control+learning in hardware), and gaining competitive edge.

Automotive & EV/energy (Tesla, Qualcomm, Apple) benefit from silicon that fuses learning and control, yielding more responsive autonomy, safer systems, improved energy-management, and better integration of vehicle+grid ecosystems.

Chip & hardware vendors (Intel, Broadcom, Qualcomm) can license/design this invention as part of their product road-maps, enabling them to launch a new product line (intelligent-control SoCs) and capture premium margins.

Software-heavy firms (Palantir, Amazon) shift into hardware-augmented control/AI systems, improving differentiation and margin.

Investor / Buyer Highlights:

Empowers OEMs and chip vendors to offer premium “intelligent-control” silicon, commanding higher ASPs and capturing adjacent systems revenue.

Opens new markets (self-healing grids, autonomous micro-satellite constellations, adaptive UAV swarms, real-time edge ML-control) with first-mover advantage.

Delivers cost-savings via ~30-70% reduction in embedded compute/energy overhead (vs conventional GPU+MCU stacks) and shorter development-cycles.

Integrates seamlessly into existing silicon supply-chains (e.g., 130 nm ? 28 nm nodes, ONNX-GDSII flow) enabling scalable commercialization.

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Whatsapp: +1-647-551-8780
Canada

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