Mixed-precision quantization, where a deep neural network's layers are quantized to different precisions, offers the opportunity to optimize the trade-offs between model size, latency, and statistical accuracy beyond what can be achieved with homogeneous-bit-width quantization. To navigate the intractable search space of mixed-precision configurations for a given network, this paper proposes a hybrid search methodology. It consists of a hardware-agnostic differentiable search algorithm followed by a hardware-aware heuristic optimization to find mixed-precision configurations latency-optimized for a specific hardware target. We evaluate our algorithm on MobileNetV1 and MobileNetV2 and deploy the resulting networks on a family of multi-core RISC-V microcontroller platforms with different hardware characteristics. We achieve up to 28.6% reduction of end-to-end latency compared to an 8-bit model at a negligible accuracy drop from a full-precision baseline on the 1000-class ImageNet dataset. We demonstrate speedups relative to an 8-bit baseline, even on systems with no hardware support for sub-byte arithmetic at negligible accuracy drop. Furthermore, we show the superiority of our approach with respect to differentiable search targeting reduced binary operation counts as a proxy for latency.

该论文提出了一种混合精度搜索方法，该方法通过硬件无关的可微分搜索算法和硬件感知优化算法来寻找特定硬件目标上的优化后的混合精度配置，以减少模型大小、延迟并保持统计准确性，该方法在MobileNetV1和MobileNetV2上进行了评估，在具有不同硬件特性的多核RISC-V微控制器平台上展示了与8位模型相比高达28.6%的端到端延迟降低，在没有对子字节算术支持的系统上也能实现加速，同时在代表延迟的减少二进制运算次数上，我们的方法也表现出优越性。

自由比特：边缘上混合精度量化神经网络的延迟优化