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Found 4 papers, 0 papers with code
Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy
This paper addresses this memory limitation by introducing an activation-aware model compression methodology that uses selective low-rank weight tensor approximations of different layers to reduce the parameter count of ViTs.
Low-Precision Mixed-Computation Models for Inference on Edge
This paper presents a mixed-computation neural network processing approach for edge applications that incorporates low-precision (low-width) Posit and low-precision fixed point (FixP) number systems.
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation
As a result, our proposed method represents a leap forward in neural network design optimization, paving the way for quick model design and implementation in settings with limited resources, thereby propelling the potential of scalable deep learning solutions.
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation
Post-training compression techniques such as pruning and quantization can help lower deployment costs.
