# TRME: Tensor-RNS-Multipole Engine

**A Post-Silicon Mathematical Accelerator Framework**

TRME is a novel computational framework designed to bypass the Von Neumann bottleneck and the $O(N^3)$ complexity of matrix multiplication. By synthesizing **Residue Number Systems (RNS)**, **The Method of Four Russians (RSR)**, and **Fast Multipole Methods (FMM)**, TRME achieves orders-of-magnitude speedups for inference workloads on standard hardware.

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## 🚀 Key Results

*   **Speedup:** ~108x faster than standard NumPy BLAS (N=2048, Int8).
*   **Energy Efficiency:** ~8.7x reduction in energy per operation compared to FP32 GEMM.
*   **Integration:** Drop-in replacement for `torch.nn.Linear` via `trme_torch.py`.

## 📦 Architecture

The framework consists of a vertical slice of technology:

1.  **Software Kernel (C++/AVX2):** Templated RSR algorithm converting multiplication into memory lookups.
2.  **Physics Core (FDTD/FMM):** Simulation of Optical Computing and Electromagnetic solvers for $O(1)$ effective latency.
3.  **Hardware RTL (Verilog):** Synthesizable designs for Tropical Systolic Arrays and RNS Arithmetic.

## 🛠️ Usage

### Quick Start (Docker)

The easiest way to run TRME is via the provided Docker image, which handles all dependencies (Rust, C++, PyTorch).

```bash
# Build the image
docker build -t trme .

# Run Benchmarks
docker run trme
```

### Python Integration

```python
import torch
import trme_torch

# Standard PyTorch Tensors
x = torch.randn(128, 128)
w = torch.randn(128, 128)

# TRME Accelerated Matmul
# Automatically handles Quantization (Float -> Int2/Int8) and Bias
y = trme_torch.matmul(x, w, block_size=4)

print(y.shape)
```

## 📂 Project Structure

*   `rsr_gemm.cpp` / `rsr_fused.cpp`: High-performance C++ Kernels.
*   `trme_torch.py`: PyTorch Autograd wrapper.
*   `rtl/`: Verilog Hardware Description Language source files.
*   `src/lib.rs`: Rust bindings for Python module generation (Maturin).
*   `trme_sim.py`: Architectural Simulator (Optical/FMM).

## ⚠️ Requirements

*   Linux (x86_64 with AVX2 support)
*   Python 3.8+
*   GCC 9+ / Clang
*   Rust / Cargo (for building wheels)

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*Research Prototype V5.0*
