Energy efficiency and memory footprint of a convolutional neural network
(CNN) implemented on a CNN inference accelerator depend on many factors,
including a weight quantization strategy (i.e., data types and bit-widths) and
mapping (i.e., placement and scheduling of DNN elementary operations on
hardware units of the accelerator). We show that enabling rich mixed
quantization schemes during the implementation can open a previously hidden
space of mappings that utilize the hardware resources more effectively. CNNs
utilizing quantized weights and activations and suitable mappings can
significantly improve trade-offs among the accuracy, energy, and memory
requirements compared to less carefully optimized CNN implementations. To find,
analyze, and exploit these mappings, we: (i) extend a general-purpose
state-of-the-art mapping tool (Timeloop) to support mixed quantization, which
is not currently available; (ii) propose an efficient multi-objective
optimization algorithm to find the most suitable bit-widths and mapping for
each DNN layer executed on the accelerator; and (iii) conduct a detailed
experimental evaluation to validate the proposed method. On two CNNs
(MobileNetV1 and MobileNetV2) and two accelerators (Eyeriss and Simba) we show
that for a given quality metric (such as the accuracy on ImageNet), energy
savings are up to 37% without any accuracy drop.

卷积神经网络（CNN）的能效和内存占用取决于权重量化策略和映射，通过启用丰富的混合量化方案，我们可以找到更有效利用硬件资源的映射，从而在精度、能耗和内存要求之间取得更好的平衡。通过扩展 Timeloop 工具，提出了一种高效的多目标优化算法，实证了该方法在两种 CNN 和两种加速器上的有效性，并证明了相对于未经精心优化的 CNN 实现，可以实现高达 37% 的能量节省，而无需降低精度。