  INT8 quantization has become one of the standard techniques for deploying
convolutional neural networks (CNNs) on edge devices to reduce the memory and
computational resource usages. By analyzing quantized performances of existing
mobile-target network architectures, we can raise an issue regarding the
importance of network architecture for optimal INT8 quantization. In this
paper, we present a new network architecture search (NAS) procedure to find a
network that guarantees both full-precision (FLOAT32) and quantized (INT8)
performances. We first propose critical but straightforward optimization method
which enables quantization-aware training (QAT) : floating-point statistic
assisting (StatAssist) and stochastic gradient boosting (GradBoost). By
integrating the gradient-based NAS with StatAssist and GradBoost, we discovered
a quantization-efficient network building block, Frost bottleneck. Furthermore,
we used Frost bottleneck as the building block for hardware-aware NAS to obtain
quantization-efficient networks, FrostNets, which show improved quantization
performances compared to other mobile-target networks while maintaining
competitive FLOAT32 performance. Our FrostNets achieve higher recognition
accuracy than existing CNNs with comparable latency when quantized, due to
higher latency reduction rate (average 65%).
