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Learn to deploy energy-efficient AI models on edge devices through this 21-minute technical presentation that demonstrates two complementary strategies for making transformers lean, fast, and deployable on Jetson-class hardware. Discover how to identify where model parameters truly reside—in feedforward layers—and leverage this insight to prune and quantize strategically for maximum efficiency gains with minimal accuracy loss. Explore MAGRIP, a task-agnostic pruning method for large language models that targets neurons within feedforward blocks using a dual-signal saliency score combining L2 magnitude for contribution assessment and Jacobian norm for sensitivity analysis, implementing staged pruning through coarse and fine passes followed by differential masking to deactivate ineffective units. Examine how this approach creates structured sparsity that optimizes cache performance and memory access while preserving attention mechanisms' relational capabilities, with benchmarks on LLaMA and Gemma models showing maintained performance across QA and reasoning tasks. Investigate BitMed ViT, a 2-bit vision transformer optimized for medical AI applications that replaces multi-head attention with multi-query attention to reduce key-value bandwidth and compresses feedforward layers to 2-bit weights, achieving 16 weights per 32-bit read through strategic packing combined with knowledge distillation. Understand practical deployment results showing up to 43x model size reduction, 22x latency improvements, and significant energy efficiency gains on Jetson hardware while maintaining baseline accuracy. Master key principles including focusing compression efforts on feedforward layers, aligning sparsity patterns with hardware-friendly structures, utilizing distillation for low-precision stability, and measuring critical metrics like memory traffic and energy consumption rather than just FLOPs for edge robotics, clinical imaging, and sensor-proximate applications.
