A Comprehensive Review of Llm Neural Network Enhancements for Advanced Driving Assistance Systems Through Quantization

Engineering notes

Key topics: full self-driving, autonomous driving, self-driving, tesla, adas, large language model, deployment. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Deep learning models, particularly Large Language Models (LLMs), have shown high degrees of complexity in recent years and have, therefore, made effective quantization techniques necessary. These are very important for the efficient deployment of models into environments of limited resources in autonomous driving and Advanced Driver Assistance Systems (ADAS). Quantization is of paramount importance in the field of ADAS, as accuracy directly relates to safety. More importantly, quantization technique advances do allow the design of safety-critical features with higher accuracy in ADAS, such as the brand new Full Self-Driving technology developed by Tesla, whose processing speeds reach over 100 frames per second and with more than 85 % accuracy. This is a survey paper that covers the latest developments in this particular area of operator quantization, which are Gradient-based Post-Training Quantization (GPTQ), Generalized Gradient Update Framework (GGUF), and Adaptive Weight Quantization (AWQ). While memory could be reduced to a low level, performance is not affected at all. Yet, most of the time, it sacrifices the accuracy of the latest advances in quantization, like PTQ or QAT. We review a plethora of advanced quantization techniques to improve both the performance and efficiency of real-world deep learning models. The aim is to demonstrate the efficiency and effectiveness achieved when advanced methods of quantization are applied to improve deep learning models for real applications thereby offering insights into promising research opportunities.

Links and sources

• Official / arXiv page

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