Skip to main content

Built for Scale, Designed for Reality

From vector databases to custom silicon — practical applications of minimal-computation architecture

For AI/ML Companies

Replace Your Vector Database

Current State: You're Hitting Walls

  • ✗Pinecone, Weaviate, Qdrant — all O(log n) or approximate nearest neighbors
  • ✗Search latency increases as you add more embeddings (logarithmic scaling)
  • ✗Approximate search trades accuracy for speed (HNSW, IVF — good enough isn't always enough)
  • ✗Storage costs compound as datasets grow (vector bloat is real)
  • ✗Reindexing takes hours when you need to update

With FEM + QER: A Different Baseline

  • •O(log log n) search complexity— provably faster than binary search
  • •Designed for exact matches— no approximate neighbors, byte-exact recall
  • •Up to ≥100:1 compression— store up to 10x more embeddings in same RAM
  • •Sub-millisecond latency— at billion-record scale on test workloads
  • •Instant updates— no reindexing, anchors crystallize automatically

Up to 100x

Faster search vs traditional vector DBs on test workloads

Up to 10-100x

Better compression (store more, pay less)

<1ms

Latency at billion-scale on test workloads

Migration Path

Potential drop-in replacement for existing vector database APIs. Ingest your embeddings via FEM, replace search calls with QER endpoints. Benchmarks show up to 100x speedup on representative RAG workloads.

Licensing available: Reference implementation, API documentation, performance validation suite included.

For Chip Companies

License the Architecture

The Problem: GPUs Aren't Enough Anymore

  • ✗AI workloads dominated by matrix multiplication (GPUs excel, but energy costs are unsustainable)
  • ✗Edge deployment constrained by power budgets (can't fit a datacenter GPU in a car)
  • ✗Inference latency unpredictable (variable-depth compute graphs)
  • ✗Custom AI chips fragmented (TPUs, NPUs, IPUs — no clear winner)

Complete Silicon Design, Ready to License

  • •Entropy Processing Unit (EPU) instruction set— custom ISA for XNOR, population count, entropy gating
  • •Tiled architecture— horizontal scaling by adding compute tiles (no architectural bottleneck)
  • •Mesh network-on-chip— low-latency tile communication, scales to hundreds of tiles
  • •CAM banks integrated on-die— content-addressable memory for QER multi-hash probing
  • •HBM integration— on-package high-bandwidth memory for FEM anchor-delta storage

Up to 30%+

Energy reduction vs GPU inference

Fixed

Latency (constant pipeline depth)

Linear

Scaling via tile addition

What You Get

  • •Complete RTL design (Verilog/SystemVerilog) for EPU, tiles, CAM banks
  • •Reference implementation validated on FPGA (benchmarks included)
  • •Instruction set architecture documentation (ISA specification)
  • •Compiler toolchain for ELF/FEM/QER algorithms → EPU assembly
  • •Energy/performance models (PPA analysis for 7nm, 5nm, 3nm nodes)

Patent-protected architecture. Licensing includes patent rights for silicon implementation. Contact us for terms.

For Enterprises

Sovereign AI Stack

The Compliance Problem

  • ✗Third-party AI APIs (OpenAI, Anthropic) = your data leaves your infrastructure
  • ✗Black-box models fail audits (GDPR, CCPA, NIST AI RMF all require explainability)
  • ✗Model retraining is expensive and slow (knowledge updates take weeks)
  • ✗Non-deterministic outputs make debugging impossible (same input, different outputs)

Sovereign AI: Auditable, On-Premise, Deterministic

  • •Your data never leaves your infrastructure— deploy on-premise or in your VPC
  • •Auditable decision trails— ELF decision trees show exactly why an output was generated
  • •No model retraining— update knowledge via FEM memory ingestion (instant)
  • •Designed for deterministic outputs— reproducible results enable debugging and testing
  • •NIST AI RMF aligned— built with risk management framework from day one

Compliance Ready

  • •GDPR: Data never leaves EU (deploy on-premise)
  • •CCPA: Auditable decision logs for transparency
  • •NIST AI RMF: Risk documentation built-in
  • •EU AI Act: Deterministic, traceable outputs

Operational Benefits

  • •Update knowledge in minutes (not weeks)
  • •Debug with confidence (deterministic = testable)
  • •Energy costs up to 30%+ lower than GPU inference
  • •Horizontal scaling (add tiles, not new clusters)

Use Cases

  • •Internal knowledge base search: Replace Elasticsearch/Solr with FEM+QER (faster, more accurate)
  • •Compliance automation: Auditable decision trails for regulatory reporting
  • •Document analysis: Process contracts, legal docs with deterministic extraction
  • •Customer support: Deterministic responses from knowledge base (no hallucinations)

For Researchers

Physics-Grounded AI

A Different Foundation

Most AI research is incremental: better optimizers, bigger models, more data. We started from physics principles — entropy, information theory, thermodynamics — and built up.

This opens new research directions that aren't possible with gradient descent and backpropagation.

Entropic First Principles

  • •Logic as entropy collapse (not optimization)
  • •Memory mass M(x) = Σ e^(-j·H(x))
  • •Resonance-based search (multi-hash CAM)
  • •Plateau detection via entropy analysis

Crystallized Knowledge Model

  • •Anchors crystallize via usage patterns
  • •Deltas encode structural variations
  • •Fractal compression with reversibility
  • •Continuous learning without forgetting

Open Research Questions

  • ▸
    Inverse Problem Solving: Given output state, reconstruct input configuration. FEM anchor-delta structure enables bounded reconstruction error. What are the complexity limits?
  • ▸
    Entropy-Driven Optimization: Can entropic gradients replace backpropagation for parameter tuning? Early results show promise for discrete search spaces.
  • ▸
    Physical Law Encoding: Encode physics equations as anchors (e.g., Maxwell's equations, Navier-Stokes). Can FEM+QER accelerate simulation and parameter inference?
  • ▸
    Contradiction Detection: Entropy analysis reveals inconsistencies in memory. Formalize this as a logic system? Applications to automated theorem proving?

Collaboration Opportunities

We're open to research collaborations with universities and labs. Areas of interest:

  • •Information theory and compression (Shannon entropy, Kolmogorov complexity)
  • •Content-addressable memory and associative computing
  • •Physics-informed AI and inverse problem solving
  • •Hardware architecture for AI (ASICs, FPGAs, neuromorphic)

Patent Application BLSHP.001PR is publicly available. Use it as a starting point for academic research. Commercial applications require licensing.

Ready to Deploy Minimal-Computation Intelligence?

Whether you're replacing a vector database, licensing silicon IP, or building sovereign AI infrastructure — let's discuss how FEM, ELF, and QER fit your needs.

Technical Deep Dive →About BLACKSHEEP OI
← Back to Home