This work presents HAWX, a hardware-aware scalable exploration framework that employs multi-level sensitivity scoring at different DNN abstraction levels (operator, filter, layer, and model) to guide selective integration of heterogeneous AxC blocks. Supported by predictive models for accuracy, power, and area, HAWX accelerates the evaluation of candidate configurations, achieving over 23* speedup in a layer-level search with two candidate approximate blocks and more than (3*106)* speedup at the filter-level search only for LeNet-5, while maintaining accuracy comparable to exhaustive search. Experiments across state-of-the-art DNN benchmarks such as VGG-11, ResNet-18, and EfficientNetLite demonstrate that the efficiency benefits of HAWX scale exponentially with network size. The HAWX hardware-aware search algorithm supports both spatial and temporal accelerator architectures, leveraging either off-the-shelf approximate components or customized designs.

翻译：本研究提出HAWX，一种硬件感知的可扩展探索框架，通过在深度神经网络的不同抽象层级（算子、滤波器、层和模型）采用多级敏感度评分，以指导异构近似计算模块的选择性集成。在精度、功耗和面积的预测模型支持下，HAWX加速了候选配置的评估过程：在仅使用两个候选近似模块的层级搜索中实现超过23倍的加速，在LeNet-5的滤波器级搜索中更获得超过3×10⁶倍的加速，同时保持与穷举搜索相当的精度。在VGG-11、ResNet-18和EfficientNetLite等前沿深度神经网络基准上的实验表明，HAWX的效率优势随网络规模呈指数级扩展。该硬件感知搜索算法同时支持空间与时间加速器架构，可利用现成的近似组件或定制化设计。