TIMELY：将PIM加速器中的数据移动和接口推向本地和时域
TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain
摘要：
基于电阻式随机存取存储器（ReRAM）的存储器中处理（R $^2$ PIM）加速器在弥合物联网设备的受限资源与卷积/深度神经网络（CNN/DNN）的高昂能量成本之间的差距方面显示出希望。具体而言，R $^2$ PIM加速器通过消除重量运动的成本并通过ReRAM的高密度提高计算密度来提高能源效率。然而，能量效率仍然受到输入和部分和（Psum）移动的主要能量成本以及数模（D/A）和模数（A/D）接口的成本的限制。在这项工作中，我们确定了R $^2$ PIM加速器中的三个节能机会：模拟数据局部性，时域接口和输入访问减少，并提出了一种创新的R $^2$ PIM加速器TIMELY，其中有三个关键贡献：（1）TIMELY在ReRAM交叉杆内采用模拟本地缓冲器（ALB），以大大增强数据局部性，最大限度地减少输入和Psum移动的能量开销;（2）分别采用时域接口（TDI）和ALB，大大降低了每次D/A（和A/D）转换的能量和转换的总次数;（3）提出了一种一次性输入读取（O $^2$ IR）映射方法，进一步降低了输入访问的能量和D/A转换的次数。使用超过10个CNN/DNN模型和各种芯片配置进行的评估表明，TIMELY在能效方面优于基准R $^2$ PIM加速器PRIME一个数量级，同时保持更好的计算密度（高达31.2 $\times$）和吞吐量（高达736.6 $\times$）。此外，进行全面的研究，以评估所提出的ALB，TDI和O $^2$ IR创新的有效性，在节能和面积减少。
英文摘要：
Resistive-random-access-memory (ReRAM) based processing-in-memory (R$^2$PIM) accelerators show promise in bridging the gap between Internet of Thing devices' constrained resources and Convolutional/Deep Neural Networks' (CNNs/DNNs') prohibitive energy cost. Specifically, R$^2$PIM accelerators enhance energy efficiency by eliminating the cost of weight movements and improving the computational density through ReRAM's high density. However, the energy efficiency is still limited by the dominant energy cost of input and partial sum (Psum) movements and the cost of digital-to-analog (D/A) and analog-to-digital (A/D) interfaces. In this work, we identify three energy-saving opportunities in R$^2$PIM accelerators: analog data locality, time-domain interfacing, and input access reduction, and propose an innovative R$^2$PIM accelerator called TIMELY, with three key contributions: (1) TIMELY adopts analog local buffers (ALBs) within ReRAM crossbars to greatly enhance the data locality, minimizing the energy overheads of both input and Psum movements; (2) TIMELY largely reduces the energy of each single D/A (and A/D) conversion and the total number of conversions by using time-domain interfaces (TDIs) and the employed ALBs, respectively; (3) we develop an only-once input read (O$^2$IR) mapping method to further decrease the energy of input accesses and the number of D/A conversions. The evaluation with more than 10 CNN/DNN models and various chip configurations shows that, TIMELY outperforms the baseline R$^2$PIM accelerator, PRIME, by one order of magnitude in energy efficiency while maintaining better computational density (up to 31.2$\times$) and throughput (up to 736.6$\times$). Furthermore, comprehensive studies are performed to evaluate the effectiveness of the proposed ALB, TDI, and O$^2$IR innovations in terms of energy savings and area reduction.
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