TensorForge: Bare-Metal Deep-Kernel Fused JIT & Memory Bandwidth Optimizer

Eliminating Memory-Bandwidth Bottlenecks and Numerical Overflows in Frontier AI Accelerators.
Automated graph pattern fusion, zero-overflow elementary reformulations, and strict double-sided saturation quantization for Tenstorrent, Triton, CUDA, and Apple Silicon MLX.

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🎯 The Problem

Frontier AI inference models (Llama 3, DeepSeek, Mistral) are heavily memory-bandwidth bound. In eager PyTorch or naive compiler backends:

1. DRAM Round-Trips: Chains of element-wise operators (RMSNorm -> SiLU -> Mul) repeatedly read and write intermediate activations back to high-latency DRAM.
2. Numerical Instability / Saturation Flaws:
   • logaddexp and logaddexp2 naive implementations evaluate $\log(\exp(a) + \exp(b))$, overflowing to $\pm\infty$ at $|x| > 88.7$.
   • Quantization kernels for uint8 often lack explicit lower-bound saturation, resulting in magnitude reflection bugs where negative numbers wrap to positive bytes.

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⚡ TensorForge Solution & Industry Benchmarks

Feature	Naive Eager Baseline (PyTorch / Standard LLKs)	TensorForge Fused JIT	Performance / Safety Gain
logaddexp Stability	Overflows to inf at $	x	> 88.7$
uint8 Quantization Saturation	Magnitude reflection on negative values	Strict $[0, 255]$ double-sided clamping	Eliminates activation corruption in INT8/FP8 models
DRAM Memory Traffic	9 round-trip tensor transfers (576 MB)	Single-pass SRAM fusion (128 MB)	4.5x DRAM traffic reduction (77.8% bandwidth saved)
Roofline Optimization	Sub-optimal DRAM thrashing	Automated Roofline regime targeting	Maximum achievable arithmetic intensity

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🛠️ Architecture

tensor-forge/
├── tensor_forge/
│   ├── kernels/
│   │   ├── overflow_safe.py     # Overflow-safe logaddexp, logaddexp2, and stable softmax
│   │   ├── quant_saturation.py  # Double-sided saturated UINT8/INT8 quantize/requantize
│   │   └── fused_ops.py         # Single-pass SRAM-resident RMSNorm + SwiGLU engine
│   ├── compiler/
│   │   ├── graph_fusion.py      # Subgraph pattern matcher and operator fusion optimizer
│   │   └── memory_planner.py    # Zero-allocation static SRAM scratchpad planner
│   └── benchmarks/
│       ├── roofline.py          # Roofline model analyzer (H100, M3 Max, Blackhole)
│       └── memory_traffic.py    # DRAM vs SRAM byte reduction benchmark

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💻 Quick Start & CLI

# Run unit tests
python3 -m unittest discover -s tests

# 1. Verify zero-overflow elementary operations
tensor-forge verify-overflow

# 2. Verify double-sided uint8 lower-bound saturation
tensor-forge verify-quant

# 3. Analyze Roofline Model on Tenstorrent Blackhole
tensor-forge roofline --hw tenstorrent_blackhole --flops 50000000 --bytes 1000000

# 4. Benchmark DRAM memory traffic reduction
tensor-forge memory-traffic --seq 4096 --dim 8192

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📄 License & Enterprise Retainers

Apache-2.0 License. Authored by Ahmed Hassan (Founder, A2Z SOC).
For bespoke kernel fusion, hardware accelerator SDKs, and inference optimization retainers, contact: ahmed@a2zsoc.com.