ENI's Configurable Neural Pipeline: A Deterministic Benchmark Plan
A research plan for measuring configurable neural acquisition, filtering, and decoding pipelines without treating unverified throughput targets as benchmark results.
Why determinism needs measured evidence
Closed-loop neural research depends on worst-case latency, not only average throughput. ENI does not yet publish a verified end-to-end deadline: each result must name the acquisition front end, channel map, sample rate, processing stages, hardware target, dataset, and measurement method.
Configuration-specific frames
Channel count and sample rate depend on the acquisition hardware and configuration. A benchmark should report the resulting input bandwidth and measure each stage separately: filtering, threshold detection, waveform extraction, feature extraction, cluster assignment, and EIPC publication.
Memory scheduling research
The research design evaluates staged buffers and hardware-specific memory placement to reduce contention. Any improvement percentage remains unverified until the repository publishes a reproducible benchmark and baseline for the named target.
Open benchmark requirements
A publishable result needs a versioned dataset, benchmark harness, target configuration, raw measurements, and license. Until those artifacts are linked and reproducible, this article describes the intended methodology rather than achieved ENI performance.
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