snn-fairness-benchmark-hardware

# SNN Fairness Benchmark with Hardware Effects First systematic fairness benchmark for Spiking Neural Networks (SNNs) that addresses the gap between algorithmic fairness research and neuromorphic hardware deployment constraints. ## Core Contribution This work introduces the first comprehensive fairness benchmark for SNNs, addressing three critical dimensions that prior assessments overlooked: 1. **Data Bias**: Demographic coverage gaps in training data 2. **Spurious Feature Leakage**: Biased proxy features (e.g., skin tone as class label) 3. **Hardware Effects**: Deployment-environment mismatches (edge devices with constrained spike encoding) ## Key Findings ### Algorithmic Fairness Gaps - Models trained on biased data exhibit **23% higher false positive rates** for underrepresented groups - Spurious feature leakage amplifies bias transfer from training to deployment ### Hardware Amplification Effects - Hardware limitations (e.g., reduced spike precision) amplify accuracy gaps by **up to 41%** in edge deployments - Loihi 2 and SpiNNaker simulators reveal different fairness degradation patterns - Resource constraints (memory, energy, latency) interact non-linearly with bias ### Mitigation Strategy Failure - Bias mitigation strategies developed for cloud-based SNNs **degrade under resource constraints** - Standard fairness interventions (re-sampling, adversarial debiasing) fail when spike precision is reduced - Hardware co-design is essential for trustworthy SNN deployment ## Benchmark Framework Components ### 1. Cross-Demographic Datasets (4 datasets) - Controlled bias injections along demographic dimensions - Bias levels: 0% (balanced), 10%, 20%, 30% coverage gaps - Evaluation metrics: accuracy parity, false positive rate disparity, demographic differential ### 2. Neuromorphic Hardware Simulators (3 platforms) - **Loihi 2**: Intel's neuromorphic research chip - **SpiNNaker**: ARM-based spiking neural network simulator - **Idealized**: Baseline without hardware constraints ### 3. SNN Models Evaluated (12 architectures) - Conversion-based SNNs (ANN-to-SNN) - Directly trained SNNs - Hybrid architectures - Parameter scales: 1M to 50M neurons ## Fairness Metrics Under Hardware Constraints | Metric | Cloud Baseline | Edge Deployment | Degradation | |--------|---------------|-----------------|-------------| | Accuracy Parity | 0.92 | 0.78 | -14% | | FPR Disparity (biased data) | 23% gap | 41% gap | +18% | | Demographic Differential | 0.05 | 0.12 | +140% | ## Hardware-Specific Fairness Patterns ### Loihi 2 - Spike precision reduction (8-bit → 4-bit) amplifies bias - Energy constraints favor shorter spike trains → reduced representation diversity - On-chip learning shows less fairness degradation than inference-only mode ### SpiNNaker - Packet routing delays affect temporal fairness across demographic groups - Batch size limitations exacerbate minority group underrepresentation - Network topology influences bias propagation patterns ## Co-Design Principles for Fair SNNs 1. **Fairness-Aware Architecture Design**: Neuron count allocation should consider demographic representation 2. **Hardware-Constrained Bias Mitigation**: Integrate fairness objectives into spike encoding optimization 3. **Multi-Level Evaluation**: Test fairness at neuron-level, layer-level, and system-level 4. **Resource Allocation Equity**: Energy/memory budget distribution should account for minority group needs ## Implementation Guidance ### Benchmark Setup ```python # Dataset bias injection bias_levels = [0.0, 0.1, 0.2, 0.3] # Coverage gap percentage demographics = ['age', 'gender', 'ethnicity', 'socioeconomic'] # Hardware simulation configuration hardware_configs = { 'loihi2': { 'spike_precision': [8, 6, 4], # bits 'energy_budget': [100, 50, 10], # mW 'memory_limit': [1024, 512, 128], # KB }, 'spinnaker': { 'packet_delay': [0, 5, 10], # ms 'batch_size': [64, 32, 16], 'cores': [4, 2, 1], } } ``` ### Fairness Evaluation Pipeline ```python # Three-stage evaluation def evaluate_snn_fairness(model, dataset, hardware_config): # Stage 1: Algorithmic fairness (no hardware constraints) alg_fairness = compute_demographic_metrics(model, dataset) # Stage 2: Hardware simulation hw_simulated = apply_hardware_constraints(model, hardware_config) # Stage 3: Fairness degradation analysis degradation = compute_fairness_gap(alg_fairness, hw_simulated) return { 'algorithmic': alg_fairness, 'hardware_simulated': hw_simulated, 'degradation_factor': degradation } ``` ## Pitfalls and Solutions ### Pitfall 1: Cloud-to-Edge Transfer Failure **Problem**: Fairness interventions optimized for cloud SNNs fail under edge constraints. **Solution**: Train fairness-aware spike encoding directly under target hardware constraints. ### Pitfall 2: Spike Precision Bias Amplification **Problem**: Lower precision disproportionately affects minority groups with sparse representations. **Solution**: Allocate higher precision budget to underrepresented demographic channels. ### Pitfall 3: Temporal Fairness Disparities **Problem**: Hardware delays cause time-varying fairness across groups. **Solution**: Implement time-sliced fairness evaluation with demographic-stratified latency metrics. ## Applications ### Healthcare SNNs - Medical imaging diagnostics on edge devices - Fairness across patient demographics (age, gender, ethnicity) - Energy-efficient inference for rural clinics ### Autonomous Systems - Perception fairness for diverse pedestrian populations - Hardware-constrained safety guarantees - Low-latency edge deployment requirements ### Biomedical Signal Processing - EEG-based diagnosis fairness across populations - Neuromorphic implant constraints - Personalized vs. population-level fairness trade-offs ## Integration with Existing SNN Frameworks ### SpikingJelly (PyTorch) ```python from spikingjelly.clock_driven import neuron, layer, functional class FairSNN(nn.Module): def __init__(self, demographic_weights): self.fairness_module = DemographicAwareLayer(demographic_weights) self.spike_encoder = neuron.LIFNode(tau=2.0, v_threshold=1.0) def forward(self, x, demographic_id): # Fairness-aware spike generation x = self.fairness_module.adjust_encoding(x, demographic_id) return self.spike_encoder(x) ``` ### Lava (Intel Loihi 2) ```python from lava.magma.core.process import Process from lava.magma.core.process.ports import InPort, OutPort class FairnessAwareSNNProcess(Process): def __init__(self, demographic_bias_factors): super().__init__() self.bias_correction = demographic_bias_factors # Hardware-aware fairness integration ``` ## Research Connections This benchmark bridges two previously disconnected domains: 1. **Algorithmic Fairness Research**: Focus on data-level and model-level interventions 2. **Neuromorphic Engineering**: Focus on energy efficiency, latency, hardware constraints The intersection reveals that fairness and hardware efficiency must be jointly optimized, not treated as separate objectives. ## Related Skills - `snn-hardware-software-codesign` - Hardware-aware SNN training - `fairness-aware-machine-learning` - General AI fairness frameworks - `neuromorphic-edge-deployment` - Edge deployment optimization - `eeg-foundation-model-adapters` - EEG-specific fairness considerations ## References - Paper: arXiv:2605.27407 - "Benchmarking Fairness in Spiking Neural Networks" - Code: https://anonymous.4open.science/r/SNN-Benchmarks-8017 - Loihi 2 Documentation: Intel Neuromorphic Research Community - SpiNNaker Tools: University of Manchester Spiking Neural Network Architecture ## Validation After creating or updating this skill, run: ```bash python3 ~/.hermes/skills/skill-creator/scripts/quick_validate.py ~/.hermes/skills/ai_collection/snn-fairness-benchmark-hardware ```