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Discrete-Difference-Core

Parallel 16-Channel 1st-Order Linear Difference Equation & Spatial Matrix Transformation RTL Core

An end-to-end, ultra-low-power hardware accelerated processing pipeline designed to convert continuous-time 1st-order differential equations into a discrete-time integer recurrence matrix. This repository features a bit-true Python simulation framework, a 65nm CMOS architectural silicon compiler, and a fully synthesizable, gate-level Verilog RTL source file.


⚡ Core Hardware Engineering Highlights

  • Zero-Decimal Optimization: All mathematical operations utilize integer coefficient scaling (Q8 format) to completely eliminate heavy Floating-Point Units (FPUs), minimizing silicon die area and dynamic power consumption.
  • Zero-Gate Interconnect Division: Division operators are entirely bypassed using fast bitwise right-shift operators (>>), implemented as hardwired physical silicon routing traces consuming 0 gate area.
  • Deterministic Execution: By eliminating conditional software loops, branches, and variable execution states, the unrolled parallel pipeline completes its calculations in perfectly predictable clock cycles.

📐 Silicon Floorplan & Processing Pipeline

16 Parallel Inputs ──► [STAGE 1: 16-Channel Parallel Recurrence Bus]│ (Difference Equations / DC Drift Stripping) ──► [STAGE 2: Spatial Transformation Matrix Engine]│ (2x16 Multiplier Array Dot Product) ──► [STAGE 3: Hardwired Bit-Slicing Networks]│ (Zero-Gate Interconnect >> Right Shifts) ──► [Final Output: [X, Y] Trajectory Coordinates]

📁 Repository Layout & Toolchain Status

  • stage1_simulation/linear_matrix_core.py
    Status: PASSED ✅ Pure Python bit-true software simulator. Verifies 1st-order linear difference recurrence loops and spatial matrix dimensions using integer-only kinematics over an active time-series timeline.
  • stage2_synthesis_estimate/physical_estimator.py
    Status: PASSED ✅ 65nm Low-Power CMOS Architectural Gate Compiler. Extracts gate-level resource counts (multipliers, adders, D-flip-flops), calculates total square core area ($µm^2$), determines core dimensions, and evaluates thermal power dissipation safety boundaries.
  • stage3_hls_compiler/generate_verilog.py
    Status: PASSED ✅ MyHDL High-Level Synthesis compilation execution script. Configures the physical hardware pin boundaries, maps parallel vector slices, and compiles the architectural blueprint into Verilog RTL.
  • bci_16ch_system.v
    Status: PRODUCED 🚀 The final production-ready Verilog RTL source file. Ready for FPGA hardware deployment or industry-standard ASIC physical Place & Route compilers.

⚡ Architectural Hardware Specifications

Parameter Layer Structural Configuration Functional Role in Silicon Pipeline
Active Channels 16 Parallel Sensor Inputs Captures high-density parallel time-series vector data
Sampling Rate 30,000 Hz (30 kHz) per channel Standard temporal baseline for high-frequency signal streams
Word Length 16-bit Signed Integers (intbv) Keeps quantization noise floor suppressed at 4.40 µV
Recurrence Order 1st-Order Difference Equation Slashes multiplier footprint in half & minimizes phase lag
Decoder Matrix $2 \times 16$ Dimensional Integer Engine Transforms high-dimensional parallel data into 2D motion planes
Synthesis Standard Verilog RTL (IEEE 1364) Ready for industry-standard ASIC Place & Route compilers

🛠️ How to Run the Compiler Pipeline

To verify the simulation metrics and re-generate the synthesizable hardware target inside your own workspace:

  1. Clone this repository and enter the directory:
    git clone https://github.com
    cd Parallel-Difference-Core
  2. Install the hardware definition compiler library:
    pip install myhdl
  3. Run the High-Level Synthesis generator to compile the Python logic into Verilog RTL:
    python stage3_hls_compiler/generate_verilog.py
  4. Run the Architectural Synthesis Compiler to evaluate the physical chip footprint constraints:
    python stage2_synthesis_estimate/physical_estimator.py

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