Shared KV caching across the last 25% to 50% of layers, adapted incrementally from pretrained LLaMA weights, is claimed to improve training speed and inference throughput with little accuracy loss.
Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition
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abstract
Compression of a neural network can help in speeding up both the training and the inference of the network. In this research, we study applying compression using low rank decomposition on network layers. Our research demonstrates that to acquire a speed up, the compression methodology should be aware of the underlying hardware as analysis should be done to choose which layers to compress. The advantage of our approach is demonstrated via a case study of compressing ResNet50 and training on full ImageNet-ILSVRC2012. We tested on two different hardware systems Nvidia V100 and Huawei Ascend910. With hardware targeted compression, results on Ascend910 showed 5.36% training speedup and 15.79% inference speed on Ascend310 with only 1% drop in accuracy compared to the original uncompressed model
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cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
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ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training
Shared KV caching across the last 25% to 50% of layers, adapted incrementally from pretrained LLaMA weights, is claimed to improve training speed and inference throughput with little accuracy loss.