AXON: Accelerated Matrix-Isolation Coprocessor Array

Overview

**SYSTEM CLASSIFICATION** Far-Edge Weight-Stationary Compute-in-Memory AI Coprocessor. **PRIMARY MISSION** To execute large-scale, 3-billion parameter AI inference models at the far edge within a 1-to-5 Watt power envelope by mapping ternary-quantized state-space mathematics directly onto commercial off-the-shelf programmable logic gates. **INDUSTRY CHALLENGE** Standard edge AI deployment is blocked by a rigid memory wall. 16-bit floating-point models require massive, power-hungry Multiply-Accumulate (MAC) circuits and dynamic Key-Value (KV) caches that explode in size during continuous sequence generation, forcing 300-Watt power draws and multi-billion-dollar custom ASIC fabrication. **HIGH-LEVEL SOLUTIONS** • **Ternary Quantization Pipeline:** Forces neural network weights into discrete {-1, 0, +1} values at 1.58 bits per parameter, mathematically eliminating all floating-point multiplication and replacing complex DSP blocks with highly efficient digital addition and subtraction circuits. • **Static State-Space Memory:** Discards unbounded Transformer KV-caches in favor of a fixed-size state vector, maintaining a constant, predictable compute-memory footprint regardless of sequence length. • **FPGA-Optimized Data Streaming:** Pairs raw programmable logic gates with low-power external DRAM, leveraging a 10.1x weight compression ratio to bypass traditional memory bandwidth bottlenecks without requiring custom silicon. **TARGET APPLICATIONS** • **Autonomous Aerospace Drones:** Real-time visual processing and high-speed obstacle avoidance without GPU-induced battery drain. • **Tactical AR Systems:** On-device spatial understanding and continuous translation bounded by strict facial thermal dissipation limits. • **Disconnected Robotics:** Localized high-speed sensor fusion and decision-making completely independent of cloud connectivity latency. **PROJECTED PERFORMANCE OBJECTIVES** • Power Objective: Sustained total system power draw bounded between 1 and 5 Watts. • Compression Objective: 10.1x memory footprint reduction, shrinking a standard 6-Gigabyte model matrix to 592.5 Megabytes. • Arithmetic Objective: 100% elimination of floating-point multiplication operations within the logic fabric. • Commercial Objective: Complete bypass of custom ASIC foundry delays via an encrypted, drop-in IP-core bitstream licensing model. **PARTNERSHIP & NDA-GATED TECHNICAL BRIEF** • **Development Status:** Subsystem Modeling and RTL Logic Validation. • **Collaboration Request:** Seeking IP licensing, strategic investment, hardware integration partners, or acquisition discussions. • **Notice:** Detailed Verilog/VHDL source code, memory bus routing logic, accumulator array architecture, and the compiled hardware bitstream are available only under NDA.

Technical Specifications

• DESIGNATION: TERRANEX NEURAL-RTL
• DEVELOPMENT STATUS: In Development
• INTELLECTUAL PROPERTY: Trade Secret / Proprietary Bitstream
• TECHNICAL REVIEW: NDA Required
• PRIMARY FUNCTION: Ultra-Low-Power Far-Edge AI Inference
• SYSTEM ARCHITECTURE: Ternary-Quantized State-Space Logic Matrix mapped to Commercial FPGAs
• TECHNOLOGY CATEGORY: Edge Computing and Hardware IP Cores
• CORE PLATFORM: Weight-Stationary Accumulator Array Platform
• INTEGRATION STRATEGY: Plug-and-Play Encrypted IP Core Bitstream Licensing
• MANUFACTURING PATH: COTS FPGA Integration (No Custom ASIC Fabrication)
• SCALABILITY PROFILE: Hardware-agnostic RTL capable of deploying onto varying commercial logic-gate densities
• TARGET APPLICATIONS: Autonomous Drones, AR Wearables, and Disconnected Robotics
• COMMERCIAL PATHWAY: Licensing / Acquisition / Co-Development
• PARTNERSHIP STATUS: Open
• INVESTMENT STATUS: Seeking Strategic Partners
• TECHNOLOGY READINESS: Subsystem Modeling

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