Tech Showcase: HDC for Low SWaP Image Classification

At Transparent AI, we're constantly exploring innovative approaches to make AI more accessible, efficient, and deployable on edge devices. Today, we're excited to share our latest experiment: implementing a hyperdimensional computing (HDC) image classification system on a Raspberry Pi 5 that performs real-time object detection using a standard webcam.

This project showcases HDC's ability to deliver impressive AI capabilities on low size, weight, and power (SWaP) hardware - a critical requirement for autonomous drones, IoT devices, and other embedded systems where computational resources are limited.

What is Hyperdimensional Computing?

Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm that represents and manipulates information using high-dimensional vectors (hypervectors) typically containing thousands of elements. Unlike traditional neural networks that learn through gradient-based optimization, HDC encodes data into these high-dimensional spaces where similarity and relationships between concepts can be efficiently computed.

Key Benefits of HDC

HDC offers several compelling advantages that make it ideal for edge computing:

• Lower computational requirements: HDC operations are primarily based on simple mathematical operations like addition and comparison rather than complex matrix multiplications, significantly reducing computational overhead.

• Minimal memory footprint: Representing objects as hypervectors is extremely memory-efficient compared to storing full neural network weights.

• One-shot/few-shot learning: HDC excels at learning from minimal examples, often requiring just a single reference image to recognize new objects - a stark contrast to traditional deep learning approaches requiring thousands of training examples.

• Federated learning capabilities: Hypervectors can be shared between devices as discrete knowledge units without exposing private data, enabling collaborative learning across distributed systems.

• Online reinforcement learning: HDC systems can continuously adapt their representations without extensive retraining, making them suitable for environments where adaptation is essential.

• Robustness to noise: The high-dimensional representation provides natural resilience against noise and variations in input data.

Our Implementation: Combining CNN Feature Extraction with HDC

For our demo, we created a hybrid system that combines the feature extraction capabilities of a convolutional neural network (CNN) with the efficiency of hyperdimensional computing:

1. Feature extraction: We use a lightweight CNN with random (untrained) weights to extract visual features from webcam images. Since the weights aren't learned, this step requires no training data or backpropagation.

2. Hypervector encoding: The extracted features are encoded into high-dimensional binary vectors (10,000 dimensions in our implementation).

3. Similarity-based classification: New images are classified by computing similarity scores (cosine or Hamming distance) between their hypervectors and stored reference hypervectors.

4. Multi-frame learning: To improve robustness, our system captures multiple frames (approximately 25) over a 5-second period when learning a new object, creating a rich representation from different angles and positions.

This approach combines the strengths of both paradigms - the powerful feature extraction of CNNs with the efficiency and one-shot learning capabilities of HDC.

Running on Raspberry Pi 5: Proving the Low SWaP Concept

To demonstrate HDC's suitability for edge computing, we deployed our system on a Raspberry Pi 5 with 16GB of RAM - a device representing the constraints of typical edge computing hardware with its ARM-based quad-core processor.

The entire system runs locally on the Pi, capturing webcam frames at 640×360 resolution, processing them through our pipeline, and displaying results in real-time.

Performance Metrics

The system's performance on the Raspberry Pi 5 exceeded our expectations:

• Baseline resources:

  • Idle power draw: 2.4W

  • Idle CPU usage: 0.7%

  • Idle RAM usage: 527MB

• Reference capture phase (when learning new objects):

  • CPU usage: 400% (utilizing all 4 cores at 100%)

  • Peak power draw: 9.1W

  • Duration: 5 seconds per object

• Detection phase (real-time inference with 3 objects):

  • CPU usage: 244% (utilizing about 2.5 cores)

  • RAM usage: 824MB (only ~300MB above baseline)

  • Sustained power draw: 7.5W

These numbers demonstrate that complex visual AI tasks can be performed on minimal hardware using HDC techniques - a task that would typically require significantly more powerful (and power-hungry) hardware with traditional deep learning approaches.

Expanded Functionality

Building on our base implementation, we've added several enhancements:

• Multi-object detection: The system can recognize multiple different objects in the reference database simultaneously.

• Explainable detection: We've implemented an invertible neural network approach that allows the system to trace back which pixels most influenced the detection decision, providing transparency in how the model makes its decisions.

• Position-invariant detection: Through data augmentation and architectural improvements, the system can recognize objects even when they're rotated or slightly repositioned.

• Adaptive threshold control: Users can dynamically adjust detection sensitivity, finding the optimal balance between false positives and false negatives.

Future Work

This project opens several exciting avenues for future development:

1. Enhanced explainability: We're expanding our invertible neural network approach to provide more detailed visualizations of feature importance in real-time.

2. Improved position invariance: We're developing techniques to make detection even more robust to position, rotation, and scaling changes.

3. Hardware acceleration: Exploring deployment on GPU-enabled edge devices like the NVIDIA Jetson Nano to further enhance performance.

4. Distributed learning: Implementing federated learning across multiple edge devices to collaboratively improve object recognition without centralizing data.

5. Reduced quantization: Investigating binary and ternary hypervectors to further reduce memory requirements.

Applications and Use Cases

This technology has numerous potential applications where traditional deep learning approaches would be impractical:

• Autonomous drones: Enabling on-device object recognition with minimal power consumption, extending flight time.

• IoT visual sensors: Allowing smart devices to recognize objects without cloud connectivity.

• Wearable AR/VR: Supporting visual recognition tasks on battery-powered headsets.

• Agricultural robots: Enabling plant/weed recognition on low-power field robots.

• Federated visual systems: Creating networks of devices that collectively learn to recognize objects while preserving data privacy.

• Remote monitoring stations: Deploying intelligent cameras in areas with limited power or connectivity.

Conclusion

Our experiment demonstrates that hyperdimensional computing represents a promising approach for deploying sophisticated AI capabilities on edge devices with strict SWaP constraints. By combining HDC with efficient feature extraction techniques, we've created a system that can learn to recognize objects with minimal examples and computational resources.

As edge AI continues to grow in importance, techniques like HDC that fundamentally rethink how we implement intelligence will become increasingly valuable. At Transparent AI, we're committed to exploring these approaches to make AI more accessible, efficient, and deployable across a wider range of devices and use cases.