Mixed-precision quantization plays a pivotal role in deploying deep neural networks in resource-constrained environments. However, the task of finding the optimal bit-width configurations for different layers under deployable mixed-precision quantization has barely been explored and remains a challenge. In this work, we present Cobits, an efficient and effective deployable mixed-precision quantization framework based on the relationship between the range of real-valued input and the range of quantized real-valued. It assigns a higher bit-width to the quantizer with a narrower quantized real-valued range and a lower bit-width to the quantizer with a wider quantized real-valued range. Cobits employs a co-learning approach to entangle and learn quantization parameters across various bit-widths, distinguishing between shared and specific parts. The shared part collaborates, while the specific part isolates precision conflicts. Additionally, we upgrade the normal quantizer to dynamic quantizer to mitigate statistical issues in the deployable mixed-precision supernet. Over the trained mixed-precision supernet, we utilize the quantized real-valued ranges to derive quantized-bit-sensitivity, which can serve as importance indicators for efficiently determining bit-width configurations, eliminating the need for iterative validation dataset evaluations. Extensive experiments show that Cobits outperforms previous state-of-the-art quantization methods on the ImageNet and COCO datasets while retaining superior efficiency. We show this approach dynamically adapts to varying bit-width and can generalize to various deployable backends. The code will be made public in https://github.com/sunnyxiaohu/cobits.

Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors. However, some vision applications of convolutional neural networks, such as always-on surveillance cameras, are critical for energy constraints. This paper aims to serve as a baseline by designing detectors to reach tradeoffs between energy and performance from two perspectives: 1) We extensively analyze various CNNs to identify low-energy architectures, including selecting activation functions, convolutions operators, and feature fusion structures on necks. These underappreciated details in past work seriously affect the energy consumption of detectors; 2) To break through the dilemmatic energy-performance problem, we propose a balanced detector driven by energy using discovered low-energy components named FemtoDet. In addition to the novel construction, we improve FemtoDet by considering convolutions and training strategy optimizations. Specifically, we develop a new instance boundary enhancement (IBE) module for convolution optimization to overcome the contradiction between the limited capacity of CNNs and detection tasks in diverse spatial representations, and propose a recursive warm-restart (RecWR) for optimizing training strategy to escape the sub-optimization of light-weight detectors by considering the data shift produced in popular augmentations. As a result, FemtoDet with only 68.77k parameters achieves a competitive score of 46.3 AP50 on PASCAL VOC and 1.11 W & 64.47 FPS on Qualcomm Snapdragon 865 CPU platforms. Extensive experiments on COCO and TJUDHD datasets indicate that the proposed method achieves competitive results in diverse scenes.