JIT Quantization

What is the primary motivation for using Just-In-Time (JIT) Quantization in machine learning training?

• To increase the accuracy of the ML models by using high precision data storage.

• To reduce memory usage and data transfer bottlenecks by quantizing data only when needed.

• To facilitate faster model inference on mobile devices by using more advanced quantization techniques.

• To avoid the use of Processing-In-Memory (PIM) and instead rely on traditional CPU-based computation.

In the context of the paper, what is one primary advantage of Processing-In-Memory (PIM) for ML training?

• It increases the precision of computations performed in machine learning models.

• It eliminates the need for quantization entirely, enabling more flexible memory usage.

• It relies exclusively on CPU and GPU resources, making it highly compatible with traditional computing setups.

• It significantly decreases the need for data to be moved between memory and processing units, reducing latency and energy consumption.

Which of the following best describes a potential trade-off when using Just-In-Time Quantization during ML training?

• Faster training times but a requirement for specialized hardware, like GPUs.

• Lower accuracy but significant reductions in memory usage.

• Increased model interpretability at the expense of data privacy.

• Higher accuracy but increased computational load on CPUs.

What is the main idea behind mixed precision training of ML models?

• Since memory is generally not a problem, storing two sets of weights is generally not an issue.

• Using lower precision weights for computation improves efficiency while maintaining high precision weights maintains accuracy.

• We avoid overfitting because of the natural limitations in precision.

• It simplifies the overall architecture, removing the need for PIM technology.

Which of the following statements best captures a limitation of the Just-In-Time Quantization approach in ML training?

• The method may lead to compatibility issues with current software frameworks and hardware.

• It increases the memory footprint and induces unnecessary data movement.

• Just-In-Time Quantization increases the model size, which makes it less efficient for memory-limited systems.

• It is optimized for inference only and has limited application in the training process of large models.