Note: This page explains the architectural rationale for deterministic computing. It does not claim regulatory approval, certification status, or clinical validation. Those activities are completed by deploying organizations using the Murray platforms.
01 The non-determinism crisis
Modern computing is fundamentally unreproducible. The same program, with the same inputs, produces different execution paths based on timing, scheduler decisions, memory layout, and hardware jitter. For consumer applications, this is merely inconvenient. For safety-critical systems, this can have serious consequences.
Unreproducible failures
When a spacecraft experiences an anomaly or a medical device exhibits unexpected behaviour, engineers often cannot reliably reconstruct execution sequences. Root cause analysis becomes challenging rather than definitive.
Significant costs per critical failureUncertifiable AI
Neural networks trained on the same data with the same hyperparameters produce different models. Inference on the same input produces different outputs. This creates significant challenges for DO-178C and ISO 26262 certification under conventional architectures.
AI faces barriers to safety-critical deploymentUnbounded legal liability
Without reproducible execution, post-incident disputes can extend for years. "What did the software do?" becomes a matter of opinion and interpretation rather than verifiable evidence.
Multi-billion-pound liability exposure in prolonged post-incident disputesHow did we get here?
Early computing was deterministic by necessity—single-core processors with simple schedulers produced predictable results. As systems grew more complex, the industry made a deliberate trade-off: sacrifice reproducibility for performance.
1960s-1970s: Single-core deterministic systems. Apollo Guidance Computer could replay mission sequences reliably.
1980s-1990s: Multi-core, pre-emptive scheduling, virtual memory. Performance increased, reproducibility disappeared.
2000s-2010s: Cloud computing, distributed systems, race conditions normalized. "Heisenbugs" became a category.
2020s: AI/ML systems fundamentally non-deterministic. Industry consensus: "Determinism is impossible." This assumption is increasingly being reconsidered.
02 What determinism actually means
Determinism is not about being "predictable" or "simple." It's about reproducibility: given identical initial state and inputs, the system is designed to produce reproducible, byte-identical outputs under defined system conditions.
∀ (S₀, I) → O, where replaying (S₀, I) always produces identical O
Where S₀ = initial state, I = input sequence, O = output sequence
What it is NOT
- Slow (performance can match or exceed non-deterministic systems)
- Simple (handles complex multi-core, ML, and distributed systems)
- Inflexible (supports dynamic adaptation and learning)
- Theoretical (production systems running 579+ days)
What it IS
- Reproducible (exact replay on different hardware)
- Verifiable (cryptographic proof of execution)
- Debuggable (failures can be reproduced reliably)
- Supports certification pathways (e.g. DO-178C, ISO 26262) through reproducible execution evidence
The breakthrough realization
The industry assumed determinism required sacrificing parallelism, performance, or flexibility. This assumption was based on trying to retrofit determinism onto non-deterministic architectures.
The Murray platforms demonstrate an alternative architectural approach: determinism designed from first principles achieves reproducibility without sacrificing performance, parallelism, or capability.
03 How deterministic systems work
Achieving determinism requires replacing every source of non-determinism with deterministic equivalents. This is not about removing features—it's about reimplementing them correctly.
Logical time replaces wall-clock time
Problem: Wall-clock time introduces non-determinism (different execution speeds produce different timestamps).
Solution: Tick-based execution where all operations occur at discrete logical time points. Replay advances ticks identically regardless of hardware speed.
Deterministic scheduling replaces OS scheduler
Problem: OS schedulers make timing-dependent decisions based on CPU load, interrupts, and other non-reproducible state.
Solution: Schedule tasks using deterministic priority schemes derived from system state, not timing or hardware events.
Controlled I/O replaces hardware interrupts
Problem: Hardware interrupts arrive at non-deterministic times, causing execution path divergence.
Solution: Timestamp and queue all inputs; replay processes inputs in identical order at identical tick counts.
Deterministic ML/RL replaces non-deterministic training
Problem: Neural network training uses randomness (weight initialization, batch shuffling, exploration) producing different models each run.
Solution: Hash-based pseudo-randomness where every "random" decision is cryptographically derived from seed state. Same seed is designed to produce reproducible model behavior.
Note: This does not imply model correctness or clinical validity — only reproducibility of training and inference processes for verification and certification purposes.
Cryptographic audit trails replace logs
Problem: Logs can be edited, deleted, or selectively presented after the fact.
Solution: Real-time cryptographic chaining (SHA-256) creates tamper-evident proof. Any modification breaks the chain.
04 Why now? Why wasn't this built before?
Deterministic computing has been theoretically possible for decades. Three factors enabled practical implementation now.
Factor 1: Certification crisis
Until recently, safety-critical systems avoided AI/ML entirely. The emergence of autonomous vehicles, AI-based medical diagnostics, and flight control AI created urgent demand for certifiable systems.
Catalyst: Recent aerospace recertification crises demonstrated the financial impossibility of continuing without deterministic architectures.
Factor 2: Cryptographic primitives matured
SHA-256, Merkle trees, and cryptographic timestamping became ubiquitous and performant enough to use in real-time systems without prohibitive overhead.
Enabler: Hardware acceleration (Intel SHA extensions, ARM Crypto Extensions) made cryptographic attestation feasible at microsecond latencies.
Factor 3: Multi-core determinism breakthrough
Industry consensus held that multi-core systems were fundamentally non-deterministic. This was disproven through bio-inspired coordination mechanisms (mycorrhizal network patterns) that achieve deterministic parallelism without locks or barriers.
Innovation: MycoEco kernel architecture demonstrates that deterministic multi-core is possible using distributed coordination with deterministic local rules.
05 Impact across industries
Deterministic computing is not a niche requirement—it's foundational infrastructure for any system where reproducibility, verifiability, or certification matters.
AI-based flight control and autonomous navigation currently face significant barriers to certification. Determinism enables pathways to certifiable AI for safety-critical aerospace applications.
Impact: Can significantly reduce fleet recertification effort through automated attestation workflows
Autonomous vehicle AI faces significant challenges achieving Level 4/5 certification without reproducible execution. Post-crash liability benefits from stronger evidence of software behavior.
Impact: Supports stronger post-incident compliance evidence through cryptographically verifiable execution records
AI-based diagnostics and adaptive therapy face regulatory hurdles without reproducible execution. Device failures can be difficult to reproduce for root cause analysis.
Impact: Can substantially reduce per-update certification effort in appropriate regulatory contexts
Deep space missions benefit greatly from reproducible execution. Communication delays require autonomous systems that can be analyzed from Earth via deterministic replay.
Impact: Improves the ability to diagnose and analyze mission-critical anomalies through reproducible replay
06 The Murray platforms
Three production-ready platforms demonstrate deterministic computing across medical devices, autonomous systems, and compliance certification.
CardioCore™
Medical Devices
Deterministic execution substrate for implantable cardiac devices. When a pacemaker fails inside a patient, deterministic replay supports root cause analysis.
Validation: 80 tests, byte-identical replay, IEC 62304 Class C aligned
MDCP™
Autonomous Systems
Complete deterministic computing platform with 14 kernels spanning AI/ML, safety, and cryptography. Enables certification pathways for autonomous systems through reproducible, verifiable execution.
Validation: 14 kernels, 545 tests, 50M ticks (579 days equivalent)
MDLCE™
Compliance Certification
Non-reinterpretable compliance attestation engine. Transforms certification from 18-month manual process to minutes of automated verification with cryptographic proof.
Validation: 579 days production, 50M ticks, Patent Claim 11 (schema binding)
07 The path forward
Deterministic computing is increasingly essential for the next generation of safety-critical systems. The question is not "if" but "when" and "who."
2025-2027: Early adopter advantage
First movers in aerospace, automotive, and medical devices capture competitive advantage through faster certification, lower liability, and AI deployment capability.
2027-2030: Growing regulatory attention
Regulatory bodies (FAA, FDA, NHTSA) are increasingly examining reproducibility and execution evidence requirements for AI-based safety-critical systems. Non-deterministic systems may face growing certification scrutiny.
2030+: Potential widespread adoption
Deterministic computing has the potential to become as fundamental as cryptography is today—assumed infrastructure for systems where reproducibility and verifiability matter.
The Murray platforms represent 579 days of production validation demonstrating that deterministic computing is feasible and practical in real-world systems. The technology exists. The question is which organizations will deploy it first.
Understanding deterministic computing
For technical discussions, architectural deep-dives, or acquisition inquiries regarding the Murray deterministic computing platforms, detailed documentation is available under NDA.