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From a Lossless (~1.5:1) Compression Algorithm for Llama2 7B Weights to Variable Precision, Variable Range, Compressed Numeric Data Types for CNNs and LLMs

This paper attempts to address and reconcile two different issues: the existence of multiple numerical data formats (such as int8, bfloat16, fp8, etc., often non optimal for the application and not directly compatible with one another) and the necessity to reduce their bandwidth requirements, especially in the case of power hungry and slow DRAM. ....

Tim Dettmers , Ruslan Svirschevski , Tim Dettimers , Jarek Duka , Furu Wei , Alexander Borzunov , Shuming Ma , John Gustafson , Vincenzo Liguori , Zhongyu Wang , Artix Ultrascale , Kintex Ultrascale , Eric Winsor , Beren Millidge , Foundation Models , Connections For Efficient Neural Networks , Ocean Logic Pty Ltd , Ocean Logic Pty , Large Language Model , Asymmetrical Numeral Systems , Flexible Numerical Data , Bit Layer Multiply Accumulator , Inference Example , Weight Compression , Open Foundation , Their Own Game ,

Developers' Hands-on | Segment Anything Quantitative Acceleration

Brand Connect ....

Ethan Yang , Neural Network Compression Framework , Intel Corporation , Meta Ai Lab , Anything Model , Training Quantization , Network Compression Framework , Post Training Quantization , Quantization Aware Training ,

The Great 8-bit Debate of Artificial Intelligence

The Great 8-bit Debate of Artificial Intelligence
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Numerical Formats For Deep Neural Networks , Editors Note , Sweet Spot , Post Training Quantization , Quantization Aware Training , Training Quantization , Aware Training , Floating Point , Numerical Formats , Deep Neural Networks , Deep Learning , Efficient Deep Learning Inference , Using Custom , Floating Point Data Types ,

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 ,