Research & Innovation in Frugal AI
The Frugal AI Hub is committed to pushing the boundaries of Frugal AI through rigorous research and innovation. We explore novel techniques and methodologies to develop AI systems that are more efficient, sustainable, and accessible. Our research support spans various areas, from model optimization to hardware acceleration, all aimed at “doing more with less” in AI.
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Key Research Areas
Model Pruning
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Description: Model pruning involves removing unnecessary weights and connections in neural networks. This reduces model size and computational complexity while preserving accuracy.
Benefits: Smaller models, faster inference, reduced energy consumption.
Example: ResNet pruning can significantly reduce the number of parameters with minimal accuracy loss.
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Quantization
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Description: Quantization converts high-precision weights (e.g., 32-bit) into lower-precision weights (e.g., 8-bit).
Benefits: Reduced model size and memory footprint, faster inference speed.
Example: INT8 quantization can lead to a substantial speedup in inference.
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Sparse & Low-Rank Approximation
Description: These techniques transform dense weight matrices into sparse representations or approximate them with lower-rank matrices.
Benefits: Reduced computational complexity and energy consumption.
Example: Sparsity techniques applied to large language models can significantly decrease energy usage.
Efficient Architectures & Hardware Optimization.
Description: This involves using AI models designed for efficiency (e.g., MobileNet, EfficientNet) and optimizing their execution on specialized AI hardware (e.g., TPUs, FPGAs).
Benefits: Improved performance with fewer computational resources.
Example: EfficientNet achieves state-of-the-art results with significantly fewer computations compared to other models.
Knowledge Distillation
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Description: Knowledge distillation trains a smaller “student” model to mimic the behavior of a larger “teacher” model.
Benefits: Smaller, faster models with comparable accuracy to larger models.
Example: BERT distillation can reduce model size while retaining a high percentage of the original model’s accuracy.
Data Parsimony & Selection
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Description: This approach focuses on using smaller, high-impact datasets, often employing techniques like active learning or smart curation.
Benefits: Reduced data labeling costs and computational demands.
Example: Active learning in medical imaging can achieve near full-data performance by labeling only a small subset of the data.
Synthetic Data & Augmentation
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Description: These methods expand datasets using artificially generated examples or transformations of existing data.
Benefits: Improved model generalization without the need for collecting more real-world data.
Example: Image augmentation techniques can enhance model robustness.
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Transfer Learning
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Description: Transfer learning involves fine-tuning pre-trained models for new tasks using smaller datasets.
Benefits: Reduced training time, data requirements, and computational resources.
Example: Fine-tuning pre-trained language models like GPT can achieve strong performance with less data.
Edge AI & TinyML
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Description: Edge AI and TinyML focus on running AI models directly on low-power hardware like sensors or microcontrollers.
Benefits: Minimized latency, energy consumption, and data transfer costs.
Example: Wake word detection on microcontrollers.
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Low-Power Hardware
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Description: This involves utilizing specialized AI chips optimized for energy efficiency (e.g., TPUs, neuromorphic processors).
Benefits: Faster and greener inference at a lower cost.
Example: Google’s Edge TPU offers high performance with low power consumption.
Neurosymbolic & Swarm Intelligence..
Description: These approaches combine logic-based reasoning with machine learning or distributed agent coordination.
Benefits: Lightweight, explainable, and distributed intelligence.
Example: Neurosymbolic AI in robotics.
Sustainable Data Centers
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Description: Deploying AI workloads in facilities powered by renewables & efficient cooling
Benefits: Supports green innovation goals and long-term TCO reduction
Example: No clear example. So, lot of opportunities here.