⚡ Cross-Modal Knowledge Distillation for Ultra-Lightweight Edge AI (Patent Pending)

This work pioneers a cross-modal knowledge distillation framework optimized for edge AI deployment, integrating modality-specific compression and lightweight feature alignment techniques.
Implemented dynamic resource allocation algorithms using PyTorch, achieving 92.3% accuracy while reducing memory footprint by 83.4% and latency by 74.5% compared to teacher models.

🔒 Patent Pending: Cross-Modal Knowledge Distillation for Ultra-Lightweight Edge AI (Filed 2025)

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🚀 Project Overview

This project explores the frontier of efficient model distillation across heterogeneous modalities (e.g., vision, audio, and sensor inputs) to create ultra-lightweight neural architectures capable of running on edge devices without sacrificing performance.

The framework introduces adaptive teacher-student pipelines that enable efficient transfer of semantic and structural knowledge across different modalities while dynamically managing on-device resources.

Key Highlights

• Achieved 92.3% inference accuracy with a 74.5% latency reduction compared to teacher models.
• Enabled deployment-ready edge inference with only 16.6% of the original memory footprint.
• Demonstrated adaptive resource scaling, sustaining 82.8% accuracy at 9.3 ms latency under constrained environments.

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🧠 Core Concepts

1. Cross-Modal Knowledge Distillation

• Transfers abstract representations between heterogeneous teacher-student pairs.
• Learns shared latent spaces through feature alignment and attention transfer.
• Enhances model robustness across varying input modalities.

2. Dynamic Resource Allocation

• Allocates compute and memory resources based on real-time load and device metrics.
• Implements latency-aware scheduling for efficient on-edge inference.
• Balances energy efficiency with accuracy retention.

3. Modality-Specific Compression

• Employs structured pruning and quantization techniques tuned for each modality.
• Integrates feature-space regularization for maintaining representational fidelity.
• Achieves superior compression without significant degradation in performance.

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• Framework: PyTorch
• Languages: Python
• Hardware: NVIDIA Jetson Nano / Edge TPU / Raspberry Pi 5
• Core Modules: Distillation Engine, Adaptive Allocator, Compression Controller

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🧩 Features

• Cross-modal teacher–student training pipeline
• Dynamic compute and memory allocation
• Modality-aware compression and pruning
• Real-time adaptive inference scheduling
• Edge-optimized deployment ready