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Found 7 papers, 3 papers with code
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions
Then, it proposes adaptive pruning policies for automatically meeting the pruning objectives of accuracy-critical, memory-constrained, and latency-sensitive tasks.
Iterative Activation-based Structured Pruning
To solve this problem, we propose two activation-based pruning methods, Iterative Activation-based Pruning (IAP) and Adaptive Iterative Activation-based Pruning (AIAP).
Adaptive Activation-based Structured Pruning
Pruning is a promising approach to compress complex deep learning models in order to deploy them on resource-constrained edge devices.
Automated Backend-Aware Post-Training Quantization
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment.
UNIT: Unifying Tensorized Instruction Compilation
However, it is hard to leverage mixed precision without hardware support because of the overhead of data casting.
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
A deep learning compiler such as Apache TVM can enable the efficient execution of model from various frameworks on various targets.
Optimizing Memory-Access Patterns for Deep Learning Accelerators
Deep learning (DL) workloads are moving towards accelerators for faster processing and lower cost.
