production use cases

Spikeforge — PC-0: Production Use Cases (no neuromorphic chip)

Umbrella for the ten chip-less production use cases. Each section is a ticket seed: enough scope to open an issue and architect it later. The shared hardware-free enablers live in production_toolkit_plan.md; the first use case is fully scoped in use_case_streaming_timeseries.md.

Design/spec only. Every claim about current code is grounded in a file/line reference so a Code-mode agent can execute this file-by-file.

Objective. Capture the ten realistic, chip-less production uses for SNN models, tie each to the spikeforge pieces that already help and the toolkit workstreams that unblock it, and open one umbrella plus one ticket per use case to be tackled later. No implementation starts here.

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1. How to read this

• Chip-less feasibility answers "does this need silicon?" — all ten are feasible on CPU/GPU/edge today; the neuromorphic chip adds power efficiency, not feasibility.
• Enablers reference the toolkit workstreams W1…W7 from production_toolkit_plan.md.
• Recommended order is easiest-to-hardest so the first ticket (streaming time series) exercises the most shared machinery.

2. Use-case index

#	Use case	Nearest fit in repo	Key enablers	Difficulty
1	Streaming time-series classification / anomaly detection	sequence_mlp, fc_small, sequence_source	W1, W2, W3	Lowest
2	Always-on audio / keyword spotting / wake-word	event bridge, EventSample	W3, W5, W6	Low–Med
3	Event-camera (DVS) vision	dvs128_gesture, EventSpikeBridge	W3, W6	Medium
4	Ultra-low-latency sensor-stream inference	simulator single loop	W3, W6	Medium
5	Anomaly / intrusion detection (IoT, network, grid)	as #1 + event_runtime	W1, W5	Medium
6	Bio-signal & medical monitoring	sequence_mlp, sequence_source	W3, W6, W7	Medium–High
7	RL for control & robotics	topology builder, NIR export	W1, W3	Medium–High
8	Efficient sequence models / spiking transformers	sequence_attn (simulation-only)	W1, W6	High
9	Edge/mobile inference under power budgets	spikeforge-targets quantize/energy	W5 (targets)	High
10	Computational neuroscience / neuromorphic R&D	TopologySpec, neuron registry, NIR	W1	High

2.1 Fully scoped specifications

Every use case now has a full specification document mirroring UC-1; each one follows the same shape (problem, reference architecture, offline/online design, the P0–P6 phases with MVP = P0–P4, dependencies, out of scope, and a ready-to-file GitHub issue payload).

#	Specification
1	use_case_streaming_timeseries.md — implemented (spikeforge 0.3.0)
2	use_case_audio_keyword_spotting.md
3	use_case_event_camera_vision.md
4	use_case_low_latency_sensor_stream.md
5	use_case_intrusion_anomaly_detection.md
6	use_case_biosignal_medical_monitoring.md
7	use_case_rl_control_robotics.md
8	use_case_spiking_transformers.md
9	use_case_edge_power_budgets.md
10	use_case_computational_neuroscience.md

3. Ticket seeds

UC-1 — Streaming time-series classification / anomaly detection

• What: classify or flag anomalies on a continuous numeric stream (vibration, telemetry, network flows), one window at a time, with low latency.
• Why SNN: temporal memory + sparse, event-driven updates; no need to buffer large frame windows.
• Chip-less: fully CPU/GPU; the canonical "run it anywhere" case.
• Repo fit: sequence_mlp over [T, B, L, D] (sequence_presets.py), toy task (sequence_source.py), encoder (spike_encoder.py).
• Enablers: W1 (stateful stream), W2 (encode-at-inference), W3 (serve).
• Scope: fully specified in use_case_streaming_timeseries.md.

UC-2 — Always-on audio / keyword spotting / wake-word

• What: continuous audio classification on a mic stream under a tight power budget (KWS, audio-event detection).
• Why SNN: streaming, low-power, event-sparse front end; a real deployed SNN niche.
• Chip-less: CPU/edge GPU; MFCC or event front end feeding the encoder.
• Repo fit: windowing via W7 I/O; event bridge (event_bridge.py); fc_small.
• Enablers: W3, W5, W6, W7.
• Scope: fully specified in use_case_audio_keyword_spotting.md.

UC-3 — Event-camera (DVS) vision

• What: gesture/eye-tracking/automotive detection on asynchronous event streams.
• Why SNN: the input is already spikes; SNNs consume sparse events natively.
• Chip-less: GPU/CPU; strongest near-term traction.
• Repo fit: dvs128_gesture, cifar10_dvs (event_datasets.md), polarity rasters, EventSpikeBridge.
• Enablers: W3, W6; optionally a DVS-camera adapter (W7).
• Scope: fully specified in use_case_event_camera_vision.md.

UC-4 — Ultra-low-latency sensor-stream inference

• What: per-sample decisions on radar/LiDAR/RF/vibration with hard latency bounds.
• Why SNN: temporal processing without buffering a full window; deterministic per-step latency.
• Chip-less: CPU/GPU; the value is scheduling/latency, not power.
• Repo fit: single temporal loop (execution.py).
• Enablers: W3, W6 (p50/p99 benchmarks are the whole point).
• Scope: fully specified in use_case_low_latency_sensor_stream.md.

UC-5 — Anomaly / intrusion detection (IoT, network, grid)

• What: flag unusual patterns in telemetry without labels.
• Why SNN: temporal dynamics + low-power always-on monitoring.
• Chip-less: CPU/edge.
• Repo fit: as UC-1 plus the sparse runtime (sparse_runner.py) and energy estimates (accounting.py).
• Enablers: W1, W5.
• Scope: fully specified in use_case_intrusion_anomaly_detection.md.

UC-6 — Bio-signal & medical monitoring

• What: EEG/ECG/EMG monitoring, seizure/arrhythmia detection, wearables.
• Why SNN: low latency at the sensor plus privacy-preserving on-device inference.
• Chip-less: CPU/wearable; regulated (e.g. MDR/FDA), so validation and traceability (W7) matter as much as accuracy.
• Repo fit: sequence_mlp; determinism (determinism.py).
• Enablers: W3, W6, W7.
• Scope: fully specified in use_case_biosignal_medical_monitoring.md.

UC-7 — Reinforcement learning for control & robotics

• What: SNN policies with temporal memory for control, trained in sim.
• Why SNN: recurrent temporal state in a compact, event-driven controller.
• Chip-less: simulation-trained; deployed on CPU/embedded.
• Repo fit: topology builder (builder.py); recurrent_net; NIR export for cross-sim.
• Enablers: W1, W3.
• Scope: fully specified in use_case_rl_control_robotics.md.

UC-8 — Efficient sequence models / spiking transformers

• What: long-sequence modeling with spiking attention.
• Why SNN: potential efficiency for very long sequences.
• Chip-less: GPU training/serving.
• Repo fit: sequence_attn — simulation-only, export refused with a typed UnsupportedStageError (stages_unmappable.py).
• Enablers: W1, W6; NIR primitive gap is upstream (not ours).
• Scope: fully specified in use_case_spiking_transformers.md.

UC-9 — Edge/mobile inference under power budgets

• What: deploy compressed SNNs on MCU/FPGA/edge CPU.
• Why SNN: sparse event-driven compute can undercut dense MACs at the edge.
• Chip-less: edge CPU/FPGA today.
• Repo fit: quantize (quantize_schemes.py), energy estimates, NIR export.
• Enablers: W5 (compression + activation quant), targets runtime.
• Scope: fully specified in use_case_edge_power_budgets.md.

UC-10 — Computational neuroscience / neuromorphic R&D

• What: brain-circuit modeling and neuromorphic algorithm research.
• Why SNN: the model is spiking dynamics.
• Chip-less: HPC/GPU clusters.
• Repo fit: TopologySpec, neuron registry (neurons/registry.py), NIR interpreter.
• Enablers: W1.
• Scope: fully specified in use_case_computational_neuroscience.md.

4. Dependency map (use cases -> toolkit workstreams)

Workstream	Unblocks
W1 stateful runtime + bundle	UC-1, UC-5, UC-7, UC-8, UC-10
W2 encode-at-inference	UC-1
W3 spikeforge-serve + clients	UC-2, UC-3, UC-4, UC-6, UC-7
W5 compression + quantization	UC-2, UC-5, UC-9
W6 observability + serving benchmarks	UC-3, UC-4, UC-6
W7 registry + I/O adapters	UC-2, UC-3, UC-6

5. Recommended order

1. UC-1 (streaming time series) — first, because it exercises W1+W2+W3, the smallest set of new primitives, and produces a reusable end-to-end reference. Implemented.
2. UC-2 (audio), UC-5 (anomaly) — reuse the UC-1 pipeline with a new I/O + objective.
3. UC-3 (DVS), UC-4 (latency) — build on the replay harness and latency gate.
4. UC-6 (medical), UC-7 (RL) — add regulation/traceability and an env adapter.
5. UC-8 (sequence), UC-9 (edge), UC-10 (neuro) — research-heavy; revisit after the toolkit lands.

Pairing rationale: each step pairs one easier ticket that reuses the UC-1 pipeline with one harder ticket that stresses a distinct platform capability (latency gate, traceability, or the export boundary), so the shared machinery is exercised before the research-heavy cases.

6. Umbrella acceptance

One umbrella issue tracks the ten seeds; every use case UC-1…UC-10 now has a fully scoped specification under plans/ (§2.1) and a ready-to-file GitHub issue payload, so a follow-up task can open one ticket per use case. No use case is implemented under this umbrella.