Scientific Abstract & System Specification
Dense activation patterns incur severe memory bandwidth bottlenecks during multi-turn agent reasoning. Polyhedral loop transformations and automatic kernel fusion eliminate redundant global memory round-trips.
Core Architectural Challenge
Optimizing code generator translating dynamic reasoning graphs into hardware-tailored sparse kernels.
Mathematical Basis & Formal Invariants
Achieved 3.4x higher token generation speed on sparse MoE models; cut VRAM footprint by 44%.
Key Empirical Findings & Benchmark Metrics
[01]
Polyhedral Transformations
[02]
Kernel Fusion
[03]
Constant Memory Execution
[04]
3.4x Speedup
Verification & Telemetry Testbed
NVIDIA H100, RTX 4090, Apple Silicon Metal Performance Shaders
Formal Verification & Engineering Stack
CUDATritonC++PyTorchLLVM
Cite this Specification (BibTeX)APA / IEEE Replicable
@techreport{georbit_sparse_neural_compiler,
title = {Eigen-Kernel: Sparse Tensor Compiler for Neural Reasoning Systems},
author = {Matrika Regmi},
institution = {Georbit Research Wing of Orbit},
year = {2024},
url = {https://georbit.org/projects/sparse-neural-compiler}
}