Hey everyone,I’ve been working on an architecture specification and high-level simulator called H-HPU (Complete Computational Acceleration Platform) designed for non-Euclidean geometries, graph operations, and relational workloads where data movement dominates ($\mathcal{O}(N \log N)$ patterns). Instead of relying on a standard 2D grid mesh, H-HPU combines a hexagonal planar topology (H6-NoC, $d \le 6$) with targeted directional coherence (H-MESI) and an adaptive closed-loop memory controller (HARC). High-level behavioral simulations ($N = 1024$ nodes) comparing H-HPU against a standard 2D Mesh baseline yielded the following performance metrics:Latency: Dropped by 53.7% (from 21.4 to 9.9 cycles) driven by a 130.9% increase in bisection bandwidth, eliminating router queue saturation at injection rates $> 0.10$. Saturation Throughput: Saturation threshold increased by 43% over the grid baseline. Coherence Overhead: H-MESI reduced invalidation traffic from 1026 messages (broadcast) down to 3.2 messages per write using a local 6-neighbor mask. Memory Pressure ($U_M$): HARC closed-loop control successfully stabilized memory pressure at $U_M \approx 0.932$ without triggering deadlocks or coherence race conditions. The full whitepaper, simulation code, and raw CSV/JSON benchmark data are available on GitHub: GitHub Repository: https://github.com/lzprograma/H-HPU Feedback from NoC researchers, computer architects, and RISC-V developers on the topological trade-offs and directional directory approach is very welcome!