ADRA：用非对称双行激活扩展数字内存计算
ADRA: Extending Digital Computing-in-Memory with Asymmetric Dual-Row-Activation
摘要：
内存计算（CiM）已经成为一种有吸引力的技术，以减轻冯诺依曼瓶颈。当前用于存储器内操作数的数字CiM方法基于用于计算逐位布尔函数和算术函数（诸如加法）的多位断言。然而，由于输入矢量到可编程电压的多对一映射，这些技术中的大多数仅限于可交换函数的CiM，而忽略了一类重要的计算，例如减法。在本文中，我们提出了一种CiM方法，它解决了映射问题，通过一个不对称的双偏置方案，使（a）同时单周期存储器读取和CiM的原始布尔函数（b）计算的任何布尔函数和（c）CiM的非交换功能，如减法和比较。虽然所提出的技术是技术不可知的，我们展示了它的效用铁电晶体管（FeFET）为基础的非易失性存储器。与标准的近存储器方法（每个操作需要两次完整的存储器访问）相比，我们证明了我们的方法可以仅使用一次存储器访问来实现满量程的双操作数数字CiM，从而导致能量延迟积（EDP）降低23.2% - 72.6%。
英文摘要：
Computing in-memory (CiM) has emerged as an attractive technique to mitigate the von-Neumann bottleneck. Current digital CiM approaches for in-memory operands are based on multi-wordline assertion for computing bit-wise Boolean functions and arithmetic functions such as addition. However, most of these techniques, due to the many-to-one mapping of input vectors to bitline voltages, are limited to CiM of commutative functions, leaving out an important class of computations such as subtraction. In this paper, we propose a CiM approach, which solves the mapping problem through an asymmetric wordline biasing scheme, enabling (a) simultaneous single-cycle memory read and CiM of primitive Boolean functions (b) computation of any Boolean function and (c) CiM of non-commutative functions such as subtraction and comparison. While the proposed technique is technology-agnostic, we show its utility for ferroelectric transistor (FeFET)-based non-volatile memory. Compared to the standard near-memory methods (which require two full memory accesses per operation), we show that our method can achieve a full scale two-operand digital CiM using just one memory access, leading to a 23.2% - 72.6% decrease in energy-delay product (EDP).
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