ADVANCED COMPUTING & MACHINE LEARNING OPTIMIZATION
Research into difficult sensing, computing, physical-system, materials, and infrastructure problems. Public descriptions intentionally omit enabling implementation details.
Sector Programs & Technical Dossiers
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LUX Monolithic Photonic-Electronic AI Accelerator [TERRANEX HYBRID-PHOTONIC]
Status: CONCEPT | Classification: UNCLASSIFIED
**SYSTEM CLASSIFICATION** Monolithic Photonic-Electronic Deep Learning Accelerator. **PRIMARY MISSION** To bypass the thermal and power delivery walls of standard silicon AI accelerators by decoupling high-speed logic from thermal interference, utilizing an ultra-low-loss optical routing plane integrated directly beneath a nanoscale digital CMOS layer. **INDUSTRY CHALLENGE** Standard deep learning accelerators are structurally limited by CMOS heat bleed. Executing heavy floating-point matrix calculations generates massive thermal interference, while routing data vertically through thick copper Through-Silicon Vias (TSVs) introduces crippling parasitic capacitance that throttles operational speed and wastes energy. **HIGH-LEVEL SOLUTIONS** • **Athermal Optical Routing:** Replaces digital matrix calculation blocks with an athermal Silicon Nitride waveguide mesh, computing linear unitary transforms natively at the speed of light while remaining physically immune to thermal phase-shift errors. • **Pockels-Effect Boundary Modulation:** Integrates epitaxial Barium Titanate thin films to execute hyper-fast phase modulation utilizing the physical Pockels effect, completely eliminating the heat generation typical of traditional thermo-optic heaters. • **Logarithmic Domain Crossing:** Bridges the optical and electrical planes using ultra-low-precision 4-bit logarithmic Analog-to-Digital Converters (ADCs), natively reading the square of the Born rule to achieve an 85% reduction in signal conversion energy. **TARGET APPLICATIONS** • **Datacenter-Scale AI Inference:** High-throughput matrix-vector multiplication processing without severe thermal cooling overhead. • **Ultra-Low-Latency Edge Computing:** Photonic processing platforms for time-critical sensor fusion and autonomous systems. • **Next-Generation Microelectronics:** Advanced heterogeneous chiplets utilizing monolithic integration over traditional 3D-bonding. **PROJECTED PERFORMANCE OBJECTIVES** • Energy Apportionment: 99.2% of computational workload allocated to the optical plane, reserving 0.8% for digital non-linear memory management. • Structural Fidelity: Phase error variance tightly constrained to prevent mathematical degradation across the physical array. • Component Footprint: Modulator interaction lengths optimized to directly match the native operating rail voltages of standard 3nm CMOS logic. • Signal Preservation: Optical cascaded stage depths physically bounded to eliminate insertion loss cascades and prevent signal decay below the noise floor. **PARTNERSHIP & NDA-GATED TECHNICAL BRIEF** • **Development Status:** Concept Validation and Subsystem Modeling. • **Collaboration Request:** Seeking co-development, licensing, investment, manufacturing, validation, or acquisition discussions. • **Notice:** Detailed calculations regarding the Photon Death Law (insertion loss), precise Pockels Voltage Gap metrics, Phase Noise Entropy cascade formulas, and 4-bit log-quantization hardware algorithms are available only under NDA.
Specifications: DESIGNATION: TERRANEX HYBRID-PHOTONIC; DEVELOPMENT STATUS: In Development; INTELLECTUAL PROPERTY: Patent Pending; TECHNICAL REVIEW: NDA Required
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AXON: Accelerated Matrix-Isolation Coprocessor Array [TERRANEX NEURAL-RTL]
Status: CONCEPT | Classification: UNCLASSIFIED
**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.
Specifications: DESIGNATION: TERRANEX NEURAL-RTL; DEVELOPMENT STATUS: In Development; INTELLECTUAL PROPERTY: Trade Secret / Proprietary Bitstream; TECHNICAL REVIEW: NDA Required
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Resource-Efficient Quantum Circuit Synthesis [REQCS]
Status: COMPUTATIONALLY IMPLEMENTED | Classification: UNCLASSIFIED
**OVERVIEW** A quantum computing research architecture developed to reduce logical qubit requirements, expensive gate operations, and computational resource costs through joint mathematical and circuit optimization. The work combines three computational implementations: reversible inverse-square-root arithmetic, finite-field decoding, and representation-aware quantum chemistry. Its central finding is that the mathematical representation of a computation can substantially influence its implementation cost. Equivalent formulations may require different numbers of quantum operations, even when they describe the same underlying mathematical result. **TECHNICAL CHALLENGE** Quantum algorithms can require significant resources because of their arithmetic, numerical precision, temporary storage, and reversible cleanup requirements. Conventional circuit optimization often begins after the mathematical formulation has been selected. This research also investigates whether a better formulation can reduce the necessary computation before circuit synthesis. **CORE INNOVATION** The approach jointly optimizes mathematical representations, reversible algorithms, numerical precision, and circuit construction. In quantum chemistry, the optimizer searches mathematically equivalent factorization coordinates with different resource-related normalization costs. In reversible arithmetic and decoding, it uses structured numerical and algebraic methods to reduce expensive operations and workspace requirements. **SYSTEM ARCHITECTURE** **Reversible Numerical Arithmetic:** Segmented quadratic approximation, centered radix-4 Booth arithmetic, optimized coefficient lookup, and measurement-based uncomputation. **Finite-Field Decoding:** Composite-field arithmetic, reversible Reed–Solomon decoding, structured polynomial operations, and optimized temporary-register management. **Quantum Chemistry:** Double-factorized Hamiltonian representations, orthogonal coordinate optimization, numerical compression, quantized rotations, and compact reversible data preparation. **PERFORMANCE** The recorded implementations achieved modeled reference-cost improvements of 9.0917× for inverse-square-root arithmetic, 8.4008× for finite-field decoding, and 1.2700× for quantum chemistry. Their combined geometric mean is 4.5947× across three distinct computational evaluations. **VALIDATION** All three implementations have recorded circuit-level verification results satisfying their respective computational requirements. The measurements concern logical quantum circuit resource models. Physical quantum hardware execution and hardware-level acceleration have not been demonstrated.
Specifications: Arithmetic Circuit: 108 logical qubits; Arithmetic Operations: 1,877 Toffoli-like; Arithmetic Verification: 200,540,928 trials; Decoder Field: GF(2¹²)
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Acoustic Parametron Ising Machine (APIM) [TERRANEX APIM]
Status: OPEN SOURCE / NOT PURSUING FABRICATION | Classification: UNCLASSIFIED
**SYSTEM CLASSIFICATION** Open-Source Analog Optimization Hardware — full build specification, published alongside the benchmark finding that led us not to build it. **PRIMARY MISSION** To determine, before spending money on hardware, whether a room-temperature analog oscillator network can outperform existing digital optimization software on the same class of problems — and to publish the answer either way. **INDUSTRY CHALLENGE** A large class of real-world problems — scheduling, routing, resource allocation, circuit placement — belong to a family where the number of possible solutions grows so fast that no digital computer can check them exhaustively. Two exotic hardware approaches have been built to attack this directly: quantum annealers, which require cooling to near absolute zero, and laser-based coherent Ising machines, which require specialized optical hardware. Both work, and both carry significant infrastructure cost. **WHAT WE BUILT AND TESTED** • **Room-Temperature Parametron Array:** A network of ordinary electronic oscillators — LC tank circuits and quartz crystals — each pumped at twice its natural frequency so it locks into one of two stable phase states, the physical analog of a binary variable. • **Wireless Coupling via Measurement Feedback:** Rather than physically wiring every pair of oscillators — infeasible past a few dozen elements — an FPGA continuously measures every cell's state and injects the computed interaction back in as a drive signal, the same approach used by published large-scale coherent Ising machines. • **Rigorous Head-to-Head Benchmarking:** Before any hardware was built, the full design was simulated and benchmarked against Toshiba's discrete Simulated Bifurcation algorithm — pure software — at matched computational cost and equal tuning effort on both sides, rather than the literature-default comparisons common in this field. **TARGET APPLICATIONS — AS EVALUATED** • **General Combinatorial Optimization:** Tested directly. No consistent advantage found against existing software at any problem size measured. • **Large Fixed-Topology Problems (untested, speculative):** Simulation suggests that if the coupling structure of a problem is fixed and physically wired rather than computed, very large instances (thousands of variables) could in principle be handled faster than any digital chip, because digital work grows with problem size and physical settling time does not. This case was not built, is not commercially pursued, and is published only as an open research direction. **PARTNERSHIP & TECHNICAL BRIEF** • **Development Status:** Simulation-complete; physical prototype not built. • **Collaboration Request:** None. This entry exists to document a negative result, not to solicit investment. • **Notice:** Every schematic, component value, benchmark instance, and line of simulation code behind this entry is public. There is no NDA-gated version of this project. • **Open Repository:** Full simulation code and benchmark suite available on GitHub at [github.com/shmuelbiller1/apim-benchmark-study](https://github.com/shmuelbiller1/apim-benchmark-study).
Specifications: DESIGNATION: TERRANEX APIM; DEVELOPMENT STATUS: Simulation Complete / Hardware Not Built; INTELLECTUAL PROPERTY: Fully open — no patents filed, no NDA; TECHNICAL REVIEW: Full benchmark paper, simulation code, and build
Technical Dossier | Deep Technical Overview | ACCESS & DOCUMENTATION