Resistive random access memory (ReRAM)-based processing-in-memory (PIM) architectures have demonstrated great potential to accelerate Deep Neural Network (DNN) training/inference. However, the computational accuracy of analog PIM is compromised due to the non-idealities, such as the conductance variation of ReRAM cells. The impact of these non-idealities worsens as the number of concurrently activated wordlines and bitlines increases. To guarantee computational accuracy, only a limited number of wordlines and bitlines of the crossbar array can be turned on concurrently, significantly reducing the achievable parallelism of the architecture. While the constraints on parallelism limit the efficiency of the accelerators, they also provide a new opportunity for fine-grained mixed-precision quantization. To enable efficient DNN inference on practical ReRAM-based accelerators, we propose an algorithm-architecture co-design framework called \underline{B}lock-\underline{W}ise mixed-precision \underline{Q}uantization (BWQ). At the algorithm level, BWQ-A introduces a mixed-precision quantization scheme at the block level, which achieves a high weight and activation compression ratio with negligible accuracy degradation. We also present the hardware architecture design BWQ-H, which leverages the low-bit-width models achieved by BWQ-A to perform high-efficiency DNN inference on ReRAM devices. BWQ-H also adopts a novel precision-aware weight mapping method to increase the ReRAM crossbar's throughput. Our evaluation demonstrates the effectiveness of BWQ, which achieves a 6.08x speedup and a 17.47x energy saving on average compared to existing ReRAM-based architectures.

翻译：基于阻变存储器（ReRAM）的存内计算（PIM）架构在加速深度神经网络（DNN）训练/推理方面展现出巨大潜力。然而，由于非理想特性（如ReRAM单元的电导变化），模拟PIM的计算精度会受到影响。随着同时激活的字线和位线数量增加，这些非理想特性的影响会加剧。为保证计算精度，交叉阵列中只能同时开启有限数量的字线和位线，从而显著降低了架构的可实现并行度。虽然并行度限制制约了加速器的效率，但也为细粒度混合精度量化提供了新的机遇。为实现高效DNN推理于实用化基于ReRAM的加速器，我们提出一种算法-架构协同设计框架，称为块级混合精度量化（BWQ）。在算法层面，BWQ-A引入了块级别的混合精度量化方案，以可忽略的精度损失实现了高权重和激活压缩比。我们还提出了硬件架构设计BWQ-H，利用BWQ-A获得的低位宽模型在ReRAM器件上执行高效DNN推理。BWQ-H还采用了一种新颖的精度感知权重映射方法，以提升ReRAM交叉阵列的吞吐量。评估结果表明，BWQ的有效性显著，与现有基于ReRAM的架构相比，平均实现了6.08倍的加速和17.47倍的能耗节省。