Recent works demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication -- the intensive and key computation in DNNs. With weights stored in the ReRAM crossbar cells as conductance, when the input vector is applied to word lines, the matrix-vector multiplication results can be generated as the current in bit lines. A key problem is that the weight can be either positive or negative, but the in-situ computation assumes all cells on each crossbar column with the same sign. The current architectures either use two ReRAM crossbars for positive and negative weights, or add an offset to weights so that all values become positive. Neither solution is ideal: they either double the cost of crossbars, or incur extra offset circuity. To better solve this problem, this paper proposes FORMS, a fine-grained ReRAM-based DNN accelerator with polarized weights. Instead of trying to represent the positive/negative weights, our key design principle is to enforce exactly what is assumed in the in-situ computation -- ensuring that all weights in the same column of a crossbar have the same sign. It naturally avoids the cost of an additional crossbar. Such weights can be nicely generated using alternating direction method of multipliers (ADMM) regularized optimization, which can exactly enforce certain patterns in DNN weights. To achieve high accuracy, we propose to use fine-grained sub-array columns, which provide a unique opportunity for input zero-skipping, significantly avoiding unnecessary computations. It also makes the hardware much easier to implement. Putting all together, with the same optimized models, FORMS achieves significant throughput improvement and speed up in frame per second over ISAAC with similar area cost.

中文翻译：

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形式：混合信号 DNN 加速器的基于细粒度极化 ReRAM 的原位计算

最近的工作证明了使用电阻随机存取存储器 (ReRAM) 作为一种新兴技术来执行固有的并行模拟域原位矩阵向量乘法的前景 - DNN 中的密集和关键计算。将权重存储在 ReRAM 交叉单元中作为电导，当输入向量应用于字线时，矩阵向量乘法结果可以作为位线中的电流生成。一个关键问题是权重可以是正数也可以是负数，但原位计算假设每个横杆列上的所有单元格都具有相同的符号。当前的架构要么使用两个 ReRAM crossbar 来表示正负权重，要么向权重添加偏移量，以便所有值都变为正数。这两种解决方案都不理想：它们要么使交叉开关的成本翻倍，要么导致额外的偏移电路。为了更好地解决这个问题，本文提出了 FORMS，一种基于 ReRAM 的细粒度 DNN 加速器，具有极化权重。我们的关键设计原则不是试图表示正/负权重，而是严格执行原位计算中的假设——确保横杆同一列中的所有权重具有相同的符号。它自然避免了额外横杆的成本。使用乘法器交替方向法 (ADMM) 正则化优化可以很好地生成此类权重，该方法可以准确地强制执行 DNN 权重中的某些模式。为了实现高精度，我们建议使用细粒度子阵列列，这为输入零跳跃提供了独特的机会，显着避免了不必要的计算。它还使硬件更容易实现。综合起来，