RolvSPARSE©
🚀Portable sparse acceleration for AI, crypto, mobile, and EVs
We’re not here to shave a few percent. We’re here to collapse compute cost and power by orders of magnitude — across the patterns that actually show up in the real world: random, block‑diagonal, banded, and power‑law.
Cross‑platform validation shows decisive speedups and energy savings on NVIDIA, AMD, and Google TPU, with correctness verified end‑to‑end.
⚙️ What RolvSPARSE© Delivers
- Breakthrough performance — Unified sparse acceleration that consistently outruns dense baselines and incumbent sparse libraries.
- Portable advantage — Works across NVIDIA (CUDA), AMD (ROCm), and Google TPU (JAX), without hardware lock‑in.
- Massive energy savings — Typical 90–99.9% reductions per iteration, turning performance per watt into a strategic weapon.
- Validated correctness — Normalized allclose checks pass across platforms and structures; results are investor‑ready.
📊 Cross‑Platform Results
Platform | Speedup vs | Speedup vs | Energy
| Dense | cuSPARSE/hipSPARSE/ | Savings
| | jaxSPARSE |
-------------------+----------------+------------------------+-----------------------
NVIDIA B200 | 28×–100× | 22×–5597× | 95%–98%, 90.9%–99.96%
(CUDA/cuSPARSE) | | |
AMD MI300X | 20×–45.8× | 9.95×–1542× | 74.8%–95.1%, 78.7%–99.85%
(ROCm/hipSPARSE) | | |
Google TPU v5e | 1.12×–1.27× | 9.15×–299.85× | 11%–21%, 89.1%–99.67%
(JAX Sparse) | | |
Patterns: random, block, banded, power‑law
✅ Interpretation: GPUs see decisive wins vs both dense and vendor sparse across structured and random patterns. TPU dense is already highly optimized on small tiles, but RolvSPARSE© still crushes JAX Sparse on memory‑hard cases.
🌍 Market Segmentation by Sparsity
- Mid‑sparsity AI (40–70% zeros): Structured N:M, block‑diagonal, banded dominate model cores; ~50% of workloads.
- High‑sparsity AI (70–99%+ zeros): Power‑law, random/top‑k attention, MoE routing, graphs, and recsys; ~50% of workloads.
📈 Weighted average speedup: ≈ 500× across incumbent deployments, ≈ 97% energy savings. 🔥 High‑sparsity planning: ≈ 1000× speedup, ≈ 99% energy savings.
🧮 The ROLV Unit: Your Efficiency Metric
ROLV Unit = (S * log10(S)) / (abs(log10(1 - D + ε))) + (E * S) / 100Where:
- S = speedup
- D = density (1 − sparsity)
- E = energy savings percentage
- ε = 1×10⁻⁶ (small epsilon to avoid division by zero)
Use the ROLV Unit to rank kernels, choose deployment paths, and communicate investor‑grade efficiency in one number.
🛡️ Patents Pending
Protected by 6 patents pending, covering applications in AI, cryptocurrency, mobile devices, and electric vehicles.
👤 Founder
Rolv E. Heggenhougen, Founder & CEO of ROLV LLC, has built over a dozen businesses across four continents, taking two public. He holds two U.S. patents (with 6 pending) and earned his degree from the University of Miami. A proven entrepreneur and innovator, Rolv now leads ROLV LLC’s mission to revolutionize sparse computing.
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