Can New Brain-Inspired AI Solve Unfamiliar Real-Time Problems and Revolutionize Energy-Efficient Computing?

1. Model Development & Simulation

Objective: Develop and optimize Spiking Neural Networks (SNNs) for real-time decision-making in constrained environments.

Step 1: Selecting the Neuromorphic Framework

• Choose an appropriate simulation environment such as NEST, Brian2, or SpiNNaker based on computational needs and hardware compatibility.
• Define neuron models and network architectures that best mimic biological processing.

Step 2: Data Preparation & Encoding

• Generate synthetic event-driven datasets or preprocess real-world sensor data.
• Convert conventional time-series data into spike-based representations (rate coding, latency coding, or phase coding).

Step 3: Model Training & Optimization

• Train SNNs using unsupervised or reinforcement learning algorithms like STDP (Spike-Timing-Dependent Plasticity) or Hebbian learning.
• Optimize for energy efficiency and decision accuracy by fine-tuning synaptic weight updates.

2. Hardware Deployment & Edge AI Implementation

Objective: Deploy optimized SNNs on neuromorphic and edge AI hardware platforms for real-time execution.

Step 4: Selecting and Preparing Hardware

• Implement SNNs on hardware such as NPU/GPU-accelerated platforms, microcontrollers, or FPGA-based neuromorphic chips (e.g., Loihi, Akida).
• Develop software interfaces to run SNN models efficiently on these platforms.

Step 5: Real-Time Inference & Adaptability Testing

• Run test scenarios where the SNN models process event-driven inputs in real-time.
• Measure computational efficiency, inference speed, and decision reliability under various operational conditions.

3. Performance Benchmarking & Comparative Analysis

Objective: Compare SNNs against conventional AI models in terms of energy efficiency, adaptability, and processing speed.

Step 6: Benchmarking Against Traditional AI Models

• Select CNNs, LSTMs, and Transformer models as baselines for comparison.
• Run both SNN and baseline models on the same tasks using identical datasets.

Step 7: Evaluating Performance Metrics

• Measure energy consumption (using power monitors and oscilloscopes).
• Measure processing latency and throughput (using profiling tools).
• Assess decision accuracy and adaptability to dynamic environments.

Step 8: Statistical Analysis & Reporting

• Perform statistical significance tests (t-tests, ANOVA) to validate performance differences.
• Document findings in structured reports and visual presentations for publication.

Additional Considerations

1. Hardware Constraints: Iterative testing will refine SNN implementations for optimal performance across different platforms.

2. Reproducibility: All code and experimental setups will be documented for future replication.

3. Scalability: If results are promising, the methodology will be extended to large-scale applications, including IoT and autonomous systems.