human
We have provided context information below.
---------------------
While neural networks have advanced the frontiers in many applications, they
often come at a high computational cost. Reducing the power and latency of neural
network inference is key if we want to integrate modern networks into edge devices
with strict power and compute requirements. Neural network quantization is one
of the most effective ways of achieving these savings but the additional noise it
induces can lead to accuracy degradation.
In this white paper, we introduce state-of-the-art algorithms for mitigating the
impact of quantization noise on the network’s performance while maintaining
low-bit weights and activations. We start with a hardware motivated introduction
to quantization and then consider two main classes of algorithms: Post-Training
Quantization (PTQ) and Quantization-Aware-Training (QAT). PTQ requires no
re-training or labelled data and is thus a lightweight push-button approach to
quantization. In most cases, PTQ is sufficient for achieving 8-bit quantization with
close to floating-point accuracy. QAT requires fine-tuning and access to labeled
training data but enables lower bit quantization with competitive results. For both
solutions, we provide tested pipelines based on existing literature and extensive
experimentation that lead to state-of-the-art performance for common deep learning
models and tasks
---------------------
Given this information, please answer the question: What is necessary to use modern neural networks in edge devices?
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