Before capital commits, architecture should earn the commitment.
Living Cipher evaluates consequential compute as a sequence of evidence-backed decisions. Software, FPGA, accelerator, component and ASIC are not roadmap stages by default. Each is a different capital commitment that must be justified by measured architectural evidence.
Silicon is an evidence outcome.
Living Cipher begins with the workload itself: what computation recurs, where conventional compute performs well, where it does not, what data must move, how state persists, what latency matters, and what deployment environment the architecture must survive.
Only after those questions are measured does specialized hardware become a candidate. CPU, GPU, NPU and DSP remain legitimate outcomes when they are sufficient.
From computation to capital decision.
Understand the recurring computation
Operation shape, invocation rate, memory behavior, temporal structure, precision, state retention and data movement are characterized before architecture is hardened.
Test programmable compute honestly
A specialized architecture must outperform a credible conventional implementation—not an intentionally weak baseline.
Build only the candidate primitive
FPGA prototypes are used to test whether the hypothesized hardware advantage survives fixed precision, physical implementation and system integration.
Promote only with evidence
Timing closure, resource use, power, throughput, admission, trust, interoperability and economics all contribute to the promotion decision.
A durable record for every candidate.
Living Cipher maintains an evidence record for candidate hardware primitives and architecture decisions. The purpose is not merely documentation. It is to preserve the reasoning linking a workload to an eventual capital commitment.
The model must earn its complexity too.
Living Cipher compares modeling and state-estimation approaches against the same workload rather than treating one algorithmic family as predetermined. Linear and robust Kalman approaches, nonlinear filters, particle and ensemble methods, switching models and learned state-space architectures can all be evaluated when the problem warrants them.
Accuracy
Does greater modeling complexity materially improve the result?
Robustness
How does the model behave under missing inputs, artifacts, drift, nonlinearity and outliers?
Compute cost
What latency, memory, arithmetic and data movement does the model require?
Hardware structural fit
Does the chosen model expose regular, reusable computation that specialized hardware could exploit?
Model selection and hardware selection are separate decisions. The best model may still belong entirely in software.
Architecture before irreversible expenditure.
By the time a function reaches an ASIC decision, the evidence should connect workload behavior, conventional-compute limits, FPGA implementation, physical design, trust, admission and deployment economics.