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

Methods

Summary

The research involves three key phases:

1. Simulation & Optimization

   • We will develop and refine SNN models using neuromorphic computing frameworks (such as NEST, Brian2), or custom-built solutions.
   • Energy efficiency, latency, and decision accuracy will be optimized through event-driven processing and adaptive weight adjustments.

2. Hardware Implementation

   • Selected SNN architectures will be deployed on edge AI hardware platforms, including NPU/GPU-based systems and low-power microcontrollers.
   • Depending on feasibility, we may explore analog neuromorphic computing approaches for further efficiency gains.

3. Benchmarking & Evaluation

   • Performance will be tested against conventional AI models in real-time scenarios, using standardized benchmark datasets.
   • Metrics will include processing speed, energy consumption, and decision accuracy under different operational conditions.

Challenges

1. Hardware constraints: Deploying SNN models on edge devices requires efficient adaptation to resource-limited environments.
Mitigation: The models will be iteratively optimized for different hardware platforms to ensure portability and scalability.

2. Data limitations: Unlike traditional deep learning, SNNs require specialized event-driven datasets.
Mitigation: We will generate synthetic datasets and use real-world sensor data where possible.

3. Evaluation complexity: Comparing SNNs to traditional AI models requires precise measurement tools.
Mitigation: We will utilize high-precision oscilloscopes, power monitors, and software profiling tools for accurate benchmarking.

Pre Analysis Plan

Hypothesis Testing:

1. Hypothesis: SNNs will demonstrate superior energy efficiency and real-time adaptability compared to traditional AI models in constrained environments.
2. We will measure and compare energy consumption, processing latency, and decision accuracy under identical test conditions.

Model Comparison:

1. SNN models will be evaluated alongside conventional deep learning architectures (e.g., CNNs and LSTMs) to quantify performance gains.
2. Statistical analysis, such as t-tests and ANOVA, will be used to confirm significant differences in efficiency.

Variance & Sensitivity Analysis:

1. We will test model performance across different hardware platforms and environmental conditions to ensure robustness.
2. Variance in decision-making reliability will be analyzed to assess adaptability under unpredictable scenarios.