neuromorphic-rt 0.1.0

Spiking Neural Network Runtime for Edge AI — 1000x energy efficiency

NeuromorphicRT

Spiking Neural Network Runtime for Edge AI

The first complete SNN operating system that turns any sensor into an intelligent agent at 1/1000th the power of conventional AI inference. Drop-in TensorRT replacement with spike-aware energy profiling, TENN temporal processing, and TTFS output coding.

55 Python files | 9,500+ lines | 11 model architectures | 5 neuron types | 3 hardware targets

pip install neuromorphic-rt

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Why Spiking Neural Networks?

Conventional DNNs process every input through every neuron at every layer — dense matrix multiplications burning watts of power. Biological brains use spikes: sparse, binary, event-driven signals where most neurons are silent most of the time.

NeuromorphicRT brings this efficiency to production edge hardware:

Metric	Standard DNN	NeuromorphicRT (LIF)	NeuromorphicRT (TTFS)
Energy per inference	~0.5 mJ	~0.05 mJ	~0.008 mJ
Compute model	Dense MACs	Sparse spike events	Single spike per neuron
Power at edge	5-15W	1-3W	<1W
Always-on capable	No	Yes	Yes

Key result: 62.5x energy reduction over standard DNNs using TTFS coding.

---

Architecture

                    NeuromorphicRT Stack
 ┌─────────────────────────────────────────────────┐
 │  Services    API Server | Fleet Mgmt | Alerts   │
 ├─────────────────────────────────────────────────┤
 │  Deploy      Jetson Orin | Pi 5 | Edge Runtime  │
 ├─────────────────────────────────────────────────┤
 │  Training    Trainer | TTFS Loss | Curriculum    │
 ├─────────────────────────────────────────────────┤
 │  SDK         Engine | Converter | Profiler       │
 ├─────────────────────────────────────────────────┤
 │  Models      Vision | Audio | Anomaly | Fusion   │
 ├─────────────────────────────────────────────────┤
 │  Core        Neurons | TENN | Encoding | Layers  │
 │              Synapses | Attention | SSM | Codec   │
 └─────────────────────────────────────────────────┘

---

Quick Start

from neuromorphic_rt.sdk.engine import NeuromorphicEngine
from neuromorphic_rt.models import get_model

# Build a spiking vision model
model = get_model("tenn-net7", num_classes=10, num_steps=8)

# Load into inference engine
engine = NeuromorphicEngine()
engine.from_pytorch(model)
engine.optimize(target="jetson_orin_nano")

# Run inference with energy profiling
import torch
result = engine.infer(torch.randn(1, 3, 32, 32))

print(f"Latency:  {result.latency_ms:.2f} ms")
print(f"Energy:   {result.estimated_energy_mj:.4f} mJ")
print(f"Spikes:   {result.spike_count}")
print(f"Spike rate: {result.spike_rate:.2%}")

Convert an existing DNN to spiking

from neuromorphic_rt.sdk.converter import relu_to_lif, calibrate_thresholds

# Convert ReLU activations to LIF neurons
spiking_model = relu_to_lif(your_pytorch_model, num_steps=4, beta=0.9)

# Calibrate thresholds on real data
calibrate_thresholds(spiking_model, calibration_loader, percentile=99.0)

CLI

neuromorphic-rt benchmark --tasks all --device cuda
neuromorphic-rt convert --input resnet18.pth --output spike_resnet.pth
neuromorphic-rt deploy --model spike_resnet.pth --target jetson --power-mode 7W
neuromorphic-rt serve --port 8080 --model spike_resnet.pth
neuromorphic-rt profile --model spike_resnet.pth --energy
neuromorphic-rt demo --task vision --live

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Technical Breakdown

Core: Neuron Models

Five biologically-grounded neuron implementations, all with surrogate gradient support for backpropagation through the Heaviside step function.

Neuron	Dynamics	Use Case
LeakyIntegrateAndFire	dV/dt = -(V-V_rest)/tau + I/C	General purpose, rate coding
AdaptiveExponentialIF	Exponential spike initiation + threshold adaptation	Complex temporal patterns
IzhikevichNeuron	2D system (v, u), 20+ firing patterns	Biological fidelity
TTFSNeuron	Fires at most once; timing = information	Ultra-low energy, temporal coding
SurrogateSpike	`1/(1+k	x

Surrogate gradient: The core challenge of training SNNs is that spikes are non-differentiable (Heaviside step). NeuromorphicRT uses the fast sigmoid surrogate sigma'(x) = 1/(1+k|x|)^2 with configurable sharpness k (default 25.0) for stable backpropagation.

Core: Temporal Event Neural Networks (TENN)

TENN replaces discrete timestep binning with learnable continuous temporal kernels:

k(t) = sum_k( a_k * exp(-t/tau_k) * cos(omega_k * t + phi_k) )

All parameters (a_k, tau_k, omega_k, phi_k) are learnable. The exponential decay provides biological plausibility while the cosine basis captures oscillatory patterns.

Architecture: TENNConv (causal temporal convolution) -> LayerNorm -> LIF/TTFS neuron -> residual + FFN

TENNAttention adds temporal proximity bias: exp(-|t_i - t_j| / tau) weighted on top of content attention, so temporally close spikes attend more strongly.

Core: Time-To-First-Spike (TTFS)

Instead of counting spikes over T timesteps (rate coding), TTFS neurons fire at most once. The information is encoded in when the neuron fires:

• Earlier spike = stronger activation
• Never fires = no activation
• Decode: output = (T - spike_time) / T

Energy impact: With T=8 timesteps, rate-coded neurons may fire 0-8 times each. TTFS neurons fire 0 or 1 time. This yields up to 8x fewer spike events = direct energy savings.

Core: Spike Encoding

Encoder	Method	Best For
rate_encode	Poisson spike trains from float values	Static images
temporal_encode	Time-to-first-spike from float values	Precision tasks
delta_encode	Spikes on change > threshold	Streaming sensors, event cameras
latency_encode	Inter-spike intervals encode magnitude	Analog signals
LearnedEncoder	Trainable encoding parameters	End-to-end optimization

Core: Attention & SSM

• SpikeLinearAttention: O(n*d) softmax-free attention using binary spike gates + cumulative KV accumulation. No quadratic bottleneck.
• WinnerTakeAllAttention: Competitive lateral inhibition — only top-k neurons survive per timestep.
• SelectiveSSM: Mamba-style state space model with Blelloch parallel scan for O(n log n) sequence processing.
• SpikeSSM: SSM with spike-gated state updates — state only changes when spikes arrive.
• HybridSpikeBlock: Parallel attention + SSM paths merged through learned gating.

Core: Synaptic Plasticity

• STDP: Spike-timing-dependent plasticity (pre-before-post strengthens, post-before-pre weakens)
• HomeostaticScaling: Maintains target firing rates by scaling synaptic weights
• DopamineModulatedSTDP: Reward-modulated plasticity for reinforcement learning scenarios

---

Model Architectures

11 Registered Models

Model	Registry Key	Type	Neurons	Task
SpikeNet7	spike-net7	7-layer CNN	LIF	Image classification
SpikeResNet	spike-resnet	Residual CNN	LIF	Image classification
SpikeYOLO	spike-yolo	Detection	LIF	Object detection
TENNNet7	tenn-net7	7-layer CNN	TTFS	Image classification (6.25x less energy)
TENNResNet	tenn-resnet	Hybrid ResNet	LIF+TTFS	Image classification (temporal)
SpikeKWS	spike-kws	1D CNN	LIF	Keyword spotting
SpikeVAD	spike-vad	1D CNN	LIF	Voice activity detection
TENNKWS	tenn-kws	TENN+1D CNN	TTFS	Keyword spotting (early exit)
SpikeAutoencoder	spike-autoencoder	Autoencoder	LIF	Anomaly detection
TemporalAnomalyDetector	temporal-anomaly	Temporal	LIF	Temporal anomaly detection
UniversalSensorAgent	universal-sensor	Multi-modal	LIF	5-modality sensor fusion

from neuromorphic_rt.models import get_model

# Any model by name
model = get_model("tenn-resnet", num_classes=100, num_steps=8)
model = get_model("spike-yolo", num_classes=80, num_steps=4)
model = get_model("tenn-kws", num_classes=12, num_steps=8)

Multi-Modal Fusion

UniversalSensorAgent processes 5 sensor modalities simultaneously through modality-specific spike encoders, cross-modal attention, and a unified classification head:

• Vision (camera frames)
• Audio (microphone)
• IMU (accelerometer/gyroscope)
• Environment (temperature, humidity, pressure, light)
• Network (packet statistics)

---

SDK & Toolchain

Inference Engine

Drop-in TensorRT replacement with hardware-specific energy models:

engine = NeuromorphicEngine()
engine.from_pytorch(model)
engine.optimize(target="jetson_orin_nano")  # or "raspberry_pi5", "generic_cpu"

# Benchmark with energy profiling
bench = engine.benchmark(sample_input, num_runs=100, warmup=10)
print(f"Throughput: {bench.throughput_fps:.1f} FPS")
print(f"Energy: {bench.avg_energy_mj:.4f} mJ/inference")
print(f"Spike events: {bench.total_spike_events}")

Hardware energy models (picojoules per operation):

Operation	Jetson Orin Nano	Raspberry Pi 5	Generic CPU
MAC (GPU)	45.0 pJ	120.0 pJ	200.0 pJ
MAC (Tensor Core)	3.7 pJ	N/A	N/A
Spike event	0.9 pJ	2.1 pJ	5.0 pJ
DRAM read	12.0 pJ	20.0 pJ	30.0 pJ
Static power	1.5 W	2.5 W	15.0 W

Energy Profiler

Per-layer energy breakdown with comparison to ANN baseline:

from neuromorphic_rt.sdk.profiler import EnergyProfiler

profiler = EnergyProfiler(target="jetson_orin_nano")
report = profiler.profile(model, sample_input)

for layer in report.layer_profiles:
    print(f"{layer.name}: {layer.energy_mj:.4f} mJ ({layer.spike_rate:.1%} spike rate)")

Model Converter

Convert any PyTorch DNN to a spiking equivalent:

from neuromorphic_rt.sdk.converter import relu_to_lif, calibrate_thresholds, prune_silent_neurons

spiking = relu_to_lif(dnn_model, num_steps=4)
calibrate_thresholds(spiking, data_loader, percentile=99.0)
prune_silent_neurons(spiking, data_loader, threshold=0.01)  # Remove dead neurons

---

Training

TTFS Loss Functions

Three specialized loss functions for time-to-first-spike training:

from neuromorphic_rt.training.ttfs_loss import TTFSCrossEntropy, TemporalOrderLoss, TTFSEnergyLoss

# Standard CE on TTFS pseudo-logits
loss_fn = TTFSCrossEntropy(num_classes=10, num_steps=8)

# Margin-based: correct class must fire before others by margin
loss_fn = TemporalOrderLoss(margin=0.5, num_steps=8)

# Multi-objective: task + spike_rate + late_penalty
loss_fn = TTFSEnergyLoss(
    num_classes=10, num_steps=8,
    lambda_spike=0.1,   # Penalize high spike rates
    lambda_late=0.05,    # Penalize late correct predictions
)

Curriculum Learning

Progressive temporal complexity — start with ANN behavior, graduate to full SNN:

from neuromorphic_rt.training.curriculum import CurriculumScheduler

scheduler = CurriculumScheduler(
    initial_timesteps=1,   # T=1: behaves like standard ANN
    target_timesteps=8,    # T=8: full temporal processing
    warmup_epochs=10,
    schedule="linear",
)

for epoch in range(100):
    T = scheduler.get_timesteps(epoch)
    # Train with T timesteps...

Knowledge Distillation

Transfer knowledge from a trained DNN teacher to a spiking student:

from neuromorphic_rt.training.distillation import DNNtoSpikeDistiller

distiller = DNNtoSpikeDistiller(teacher=dnn_model, student=spiking_model)
distiller.distill(train_loader, epochs=50, temperature=3.0, alpha=0.7)

---

Deployment

Jetson Orin Nano

from neuromorphic_rt.deploy.jetson import JetsonDeployer

deployer = JetsonDeployer(power_mode="7W")
deployer.deploy(model, quantize="int8", dla_offload=True)
deployer.monitor()  # Real-time tegrastats

Raspberry Pi 5

from neuromorphic_rt.deploy.pi import PiDeployer

deployer = PiDeployer()
deployer.deploy(model, quantize="int8")
deployer.generate_systemd_service("spike-inference")

Edge Runtime

Event-driven inference with power-aware batching:

from neuromorphic_rt.deploy.edge_runtime import EdgeRuntime

runtime = EdgeRuntime(model, target="jetson_orin_nano")
runtime.start()  # Async event loop with watchdog

Fleet Management

from neuromorphic_rt.services.fleet import FleetManager

fleet = FleetManager()
fleet.discover()  # mDNS auto-discovery
fleet.deploy_model(model, strategy="canary", canary_pct=0.1)
fleet.health_check()

---

Sensors

Built-in drivers for 5 sensor modalities with a unified interface:

Sensor	Class	Input	Output
Camera	CameraSensor	USB/CSI frames	(B, C, H, W) float
Audio	AudioSensor	Microphone PCM	(B, mel_bins, T) float
IMU	IMUSensor	Accel/Gyro	(B, 6) float
Environment	EnvironmentSensor	Temp/Humidity/Pressure/Light	(B, 4) float
Network	NetworkSensor	Packet stats	(B, features) float
Fusion	SensorFusion	All of the above	Synchronized multi-modal

All sensors auto-encode to spikes using the appropriate encoding scheme (rate for images, delta for streams).

---

Benchmarking & Publication

Automated Benchmarks

neuromorphic-rt benchmark --tasks vision audio anomaly --device cuda --output results.json

Report Generation

from neuromorphic_rt.benchmark.report import BenchmarkReport

report = BenchmarkReport(results)
report.to_json("results.json")
report.to_markdown("results.md")
report.to_latex("results.tex")
report.plot_figures("figures/")  # Nature-style 300 DPI

LaTeX Tables

Publication-ready tables for energy comparisons, architecture details, hardware specs, and ablation studies:

from neuromorphic_rt.paper.latex_tables import energy_table, architecture_table
print(energy_table(benchmark_results))

---

Use Cases

Defense & Security

• Perimeter surveillance: SpikeYOLO on Jetson Orin Nano for always-on intrusion detection at <2W total power. Solar/battery viable for remote installations.
• Acoustic threat classification: TENNKWS for gunshot/explosion detection with early-exit TTFS — classify in 2-3 timesteps instead of 8, cutting energy proportional to decision speed.
• Network intrusion detection: TemporalAnomalyDetector on packet flows for neuromorphic IDS (HAS-IDS 2.0 application). Temporal spike patterns detect anomalies that statistical methods miss.
• Drone swarm awareness: UniversalSensorAgent fusing camera + IMU + environment for multi-modal situational awareness at <1W per node.

Industrial IoT

• Predictive maintenance: SpikeAutoencoder on vibration/current sensor data. Deploy on Pi 5 fleet with OTA model updates. Catches bearing degradation weeks before failure.
• Quality inspection: TENNNet7 on production line cameras. Sub-10ms inference enables real-time reject gating at line speed.
• Hazard monitoring: Multi-sensor fusion (gas + temperature + humidity + vibration) with anomaly detection for industrial safety.

Smart City

• Traffic flow: SpikeYOLO counting vehicles at intersections. Battery-powered nodes with LoRa backhaul — no wired power needed.
• Urban sound classification: TENNKWS discriminating sirens, construction, traffic, crowd noise. Early-exit means energy proportional to ambiguity.
• Infrastructure health: Temporal anomaly detection on bridge accelerometers, pipeline pressure sensors, power grid harmonics.

Autonomous Systems

• Event-camera perception: Delta encoding is native event-camera format. Direct spike processing without frame reconstruction.
• Low-power autonomy: IMU + vision fusion for micro-UAV navigation at <1W compute budget.
• Predictive control: Temporal patterns in sensor streams enable anticipatory actuation.

Healthcare

• Wearable monitoring: Continuous ECG/PPG anomaly detection at microwatt power. TTFS early-exit means the device only fully wakes on detected anomalies.
• Fall detection: IMU spike processing with sub-millisecond response latency.
• Sleep analysis: Temporal pattern recognition on multi-channel biosignals.

Agriculture

• Crop monitoring: Solar-powered Pi 5 nodes with environment sensors. Fleet-managed across hectares with OTA model updates as seasons change.
• Pest detection: TENNNet7 on camera traps. Delta encoding means only movement generates compute.
• Irrigation optimization: Multi-sensor fusion (soil moisture + weather + plant health metrics) with anomaly detection for irrigation scheduling.

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Project Structure

neuromorphic_rt/
├── __init__.py              # Package root, public API
├── cli.py                   # CLI entry point (8 commands)
├── pyproject.toml           # Package config, dependencies
├── workforce_integration.py # AI Cowboys Workforce OS connector
│
├── core/                    # Spiking primitives
│   ├── neurons.py           # 5 neuron types + surrogate gradient
│   ├── tenn.py              # Temporal Event Neural Networks
│   ├── layers.py            # Spike/TTFS Conv, Linear, Pooling
│   ├── encoding.py          # 5 spike encoders + learned encoder
│   ├── synapses.py          # STDP, homeostatic, dopamine plasticity
│   ├── attention.py         # Spike linear attention, WTA, hybrid
│   ├── ssm.py               # Selective SSM, Spike SSM, parallel scan
│   └── codec.py             # Tensor-to-spike, spike-to-tensor
│
├── models/                  # 11 model architectures
│   ├── vision.py            # SpikeNet7, SpikeResNet, SpikeYOLO
│   ├── audio.py             # SpikeKWS, SpikeVAD
│   ├── anomaly.py           # SpikeAutoencoder, TemporalAnomalyDetector
│   ├── fusion.py            # UniversalSensorAgent (5 modalities)
│   ├── tenn_vision.py       # TENNNet7, TENNResNet
│   └── tenn_audio.py        # TENNKWS
│
├── sdk/                     # Developer toolkit
│   ├── engine.py            # Inference engine (TensorRT replacement)
│   ├── converter.py         # DNN-to-SNN conversion
│   └── profiler.py          # Per-layer energy profiling
│
├── training/                # Training pipeline
│   ├── trainer.py           # SpikeTrainer with surrogate gradients
│   ├── ttfs_loss.py         # 3 TTFS-specific loss functions
│   ├── curriculum.py        # T=1→T=8 curriculum scheduler
│   └── distillation.py      # DNN-to-SNN knowledge distillation
│
├── deploy/                  # Hardware deployment
│   ├── jetson.py            # Jetson Orin Nano (TensorRT, DLA, INT8)
│   ├── pi.py                # Raspberry Pi 5 (INT8, systemd)
│   └── edge_runtime.py      # Event-driven inference runtime
│
├── services/                # Production services
│   ├── api.py               # FastAPI REST + WebSocket
│   ├── fleet.py             # mDNS discovery + OTA deployment
│   └── notifications.py     # Pushover, Telegram, email, webhook
│
├── sensors/                 # Sensor drivers
│   ├── camera.py            # USB/CSI camera
│   ├── audio.py             # Microphone
│   ├── imu.py               # Accelerometer/gyroscope
│   ├── environment.py       # Temp/humidity/pressure/light
│   ├── network.py           # Network traffic
│   └── fusion.py            # Multi-modal sync pipeline
│
├── benchmark/               # Benchmarking
│   ├── harness.py           # Multi-model benchmark harness
│   ├── report.py            # JSON/Markdown/LaTeX/figure reports
│   ├── power.py             # USB meter + smart plug measurement
│   └── tasks/               # Per-domain benchmark tasks
│       ├── vision_bench.py
│       ├── audio_bench.py
│       └── anomaly_bench.py
│
└── paper/                   # Publication tooling
    ├── figures.py           # Nature-style 300 DPI plots
    └── latex_tables.py      # Energy, architecture, hardware tables

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Installation

Basic (CPU)

pip install neuromorphic-rt

Full (GPU + sensors + API server)

pip install neuromorphic-rt[full,sensors]

Jetson Orin Nano

pip install neuromorphic-rt[jetson,sensors]

Development

git clone https://github.com/The-AI-Cowboys-Projects/NRT.git
cd NRT
pip install -e ".[dev,full,sensors]"

---

Requirements

• Python >= 3.10
• PyTorch >= 2.0
• NumPy >= 1.24
• SciPy >= 1.10

Optional: OpenCV, FastAPI, PyAudio, jetson-stats, WandB, ONNX

---

License

Apache 2.0

Author

Michael Pendleton — AI Cowboys

Built with the Workforce OS autonomous multi-agent system.