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8T SRAM Cell as a Multibit Dot-Product Engine for Beyond Von Neumann Computing

机译:8T SRAM单元作为超越冯·诺依曼计算的多位点积引擎

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Large-scale digital computing almost exclusively relies on the von Neumann architecture, which comprises separate units for storage and computations. The energy-expensive transfer of data from the memory units to the computing cores results in the well-known von Neumann bottleneck. Various approaches aimed toward bypassing the von Neumann bottleneck are being extensively explored in the literature. These include in-memory computing based on CMOS and beyond CMOS technologies, wherein by making modifications to the memory array, vector computations can be carried out as close to the memory units as possible. Interestingly, in-memory techniques based on CMOS technology are of special importance due to the ubiquitous presence of field-effect transistors and the resultant ease of large-scale manufacturing and commercialization. On the other hand, perhaps the most important computation required for applications such as machine learning, etc., comprises the dot-product operation. Emerging nonvolatile memristive technologies have been shown to be very efficient in computing analog dot products in an in situ fashion. The memristive analog computation of the dot product results in much faster operation as opposed to digital vector in-memory bitwise Boolean computations. However, challenges with respect to large-scale manufacturing coupled with the limited endurance of memristors have hindered rapid commercialization of memristive-based computing solutions. In this paper, we show that the standard 8 transistor (8T) digital SRAM array can be configured as an analoglike in-memory multibit dot-product engine (DPE). By applying appropriate analog voltages to the read ports of the 8T SRAM array and sensing the output current, an approximate analog-digital DPE can be implemented. We present two different configurations for enabling multibit dot-product computations in the 8T SRAM cell array, without modifying the standard bit-cell structure. We also demonstrate the robustness of the present proposal in presence of nonidealities such as the effect of line resistances and transistor threshold voltage variations. Since our proposal preserves the standard 8T-SRAM array structure, it can be used as a storage element with standard read-write instructions and also as an on-demand analoglike dot-product accelerator.
机译:大规模数字计算几乎完全依赖于冯·诺依曼架构,该架构包括用于存储和计算的独立单元。数据从存储单元到计算核心的耗费能量的传输导致了众所周知的冯·诺依曼瓶颈。文献中广泛探索了各种旨在绕开冯·诺伊曼瓶颈的方法。这些包括基于CMOS和CMOS技术以外的内存中计算,其中通过对存储阵列进行修改,可以在尽可能靠近存储单元的位置进行矢量计算。有趣的是,由于场效应晶体管的普遍存在以及随之而来的大规模制造和商业化的简便性,基于CMOS技术的内存技术特别重要。另一方面,应用程序(例如机器学习等)所需的最重要的计算可能包括点积运算。业已证明,新兴的非易失性忆阻技术在现场计算模拟点产品方面非常有效。与数字矢量内存中按位布尔计算相反,点积的忆阻模拟计算导致更快的运算速度。然而,关于大规模制造的挑战以及忆阻器的有限耐久性阻碍了基于忆阻的计算解决方案的快速商业化。在本文中,我们证明了可以将标准的8晶体管(8T)数字SRAM阵列配置为类似内存的多位点积引擎(DPE)。通过将适当的模拟电压施加到8T SRAM阵列的读取端口并感测输出电流,可以实现近似的模数DPE。我们提供了两种不同的配置,可在不修改标准位单元结构的情况下在8T SRAM单元阵列中实现多位点积计算。我们还证明了在存在非理想性(例如线电阻和晶体管阈值电压变化的影响)的情况下本提案的鲁棒性。由于我们的建议保留了标准的8T-SRAM阵列结构,因此可以用作具有标准读写指令的存储元件,也可以用作按需点播类点积加速器。

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