Mixed-precision quantization, where more sensitive layers are kept at higher precision, can
achieve the trade-off between accuracy and complexity of neural networks. However, the …

In the realm of deep neural network deployment, low-bit quantization presents a promising
avenue for enhancing computational efficiency. However, it often hinges on the availability …

Quantization has enabled the widespread implementation of deep learning algorithms on
resource-constrained Internet of Things (IoT) devices, which compresses neural networks by …

Though low-bit quantization enables efficient storage and inference of deep neural
networks, it often requires the use of training data to maintain resilience against quantization …

Data-free quantization (DFQ), which facilitates model quantization without real data to
address increasing concerns about data security, has garnered significant attention within …

Time series anomaly prediction plays an essential role in many real-world scenarios, such
as environmental prevention and prompt maintenance of cyber-physical systems. However …

In this paper, we propose the BAG (Bit-width Aware Generator) and the Intermediate Layer
Knowledge Distillation using Channel-wise Attention to reduce the knowledge gap between …