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Open challenges in LLM research

Never before in my life had I seen so many smart people working on the same goal: making LLMs better. After talking to many people working in both industry and academia, I noticed the 10 major research directions that emerged. The first two directions, hallucinations and context learning, are probably the most talked about today. I’m the most excited about numbers 3 (multimodality), 5 (new architecture), and 6 (GPU alternatives). ....

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Large Transformer Model Inference Optimization

Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but very expensive to train and use. The extremely high inference cost, in both time and memory, is a big bottleneck for adopting a powerful transformer for solving real-world tasks at scale.
Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA models, there are two main factors contributing to the inference challenge (Pope et al. ....

Noam Shazeer , Zhou Ma , Zhu Gupta , Elsen Hooker , Zeroquant Yao , Xiao Lin , Xiao Lin Smoothquant , Frantar Alistarh , Smoothquant Xiao Lin , Frankle Carbin , Neural Network Compression , Trainable Neural Networks , Sinkhorn Sorting Network , A Survey , Neural Networks , Training Quantization , Aware Training , Optimal Brain Quantization , Layer By Knowledge Distillation , Lottery Ticket Hypothesis , Gradual Magnitude Pruning , Ticket Hypothesis , Straight Through Estimator , Scaling Transformer , Vision Moe , Vision Transformer ,

"TreeNet Based Fast Task Decomposition for Resource-Constrained Edge In" by Dong Lu, Yanlong Zhai et al.

Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network’s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common featur ....

Adaptation Models , Computational Modeling , Deep Learning , Edge Computing , Edge Intelligence , Odel Acceleration , Model Compression , Neural Networks , Resource Constrained , Task Analysis ,

"TreeNet: A hierarchical deep learning model to facilitate edge intelli" by Dong Lu, Yanlong Zhai et al.

Deep learning has achieved remarkable successes in various areas such as computer vision and natural language processing. Many sophisticated models have been proposed to improve performance by designing a significant number of layers of neurons. As an emerging research area, edge intelligence tries to bring intelligence to the network edge by integrating edge computing and AI technologies and it has gained wide attention for its lower latency and better privacy preservation features. Nevertheless, training and inferencing deep neural networks require intensive computation power and time, making it quite challenging to run the models on the resource-constrained edge devices. In this paper, we propose a deep learning model, namely TreeNet, based on task decomposition. After obtaining a task, we would not fit the entire task but decompose the task into disjoint sub-tasks to reduce the complexity of the required deep learning model (it could be divided multiple times if necessary). We firs ....

Deep Learning , Edge Computing , Edge Intelligence , Odel Acceleration , Model Compression , Resource Constrained ,