human
# Instruction
Summarize the following set of paragraphs as short bullet points.

# Paragraphs

Our whitepaper  compares the efficiency of floating point and integer quantization. For training, the floating-point formats FP16 and FP32 are commonly used as they have high enough accuracy, and no hyper-parameters. They mostly work out of the box, making them easy to use. 
Going down in the number of bits improves the efficiency of networks greatly, but the ease-of-use advantage disappears. For formats like INT8 and FP8, you have to set hyper-parameters for the representable range of the distributions. To get your original network accuracy back, you also have to spend some extra time quantizing these networks. Either in some simple quantization steps called post-training-quantitation (PTQ), or by training the network in a quantized way all together, called quantization-aware-training (QAT).

Given that most training in the industry is currently conducted with entire networks in FP32, or sometimes FP16 with mixed precision, the step to having some parts of a network run in FP8 is an appealing potential speed-up for the costly and time-intensive training procedures in deep learning. This topic has gained quite some traction lately, so we set out to find out what this development means for efficient inference on edge devices. Specifically, we look at both the hardware considerations for the formats and the effect of the chosen formats on neural network accuracy.
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open