具有基于SOT-MRAM的随机计算的内存中乘法引擎
In-memory multiplication engine with SOT-MRAM based stochastic computing
摘要：
内存处理（PIM）被证明是一个很有前途的解决方案，以突破内存墙和电源墙。虽然现有的PIM设计在存储器中本地产生逐位布尔逻辑运算的成功实现，但是难以以快速且有效的方式完成乘法（MUL）指令。在本文中，我们提出了一个新的随机计算（SC）设计，执行MUL与内存操作。代替使用随机数发生器（SNG），我们利用磁性随机存取存储器（MRAM）的存储器写入行为中固有的随机性。每个存储器位用作SC引擎，以写入电压脉冲的形式对操作数执行MUL，并原位存储MUL结果。与会话式SC方法相比，所提出的设计提供了高达4倍的性能改进，并且实现了18倍的加速比，而不是仅使用存储器中的按位布尔逻辑运算来实现MUL。
英文摘要：
Processing-in-memory (PIM) turns out to be a promising solution to breakthrough the memory wall and the power wall. While prior PIM designs yield successful implementation of bitwise Boolean logic operations locally in memory, it is difficult to accomplish the multiplication (MUL) instruction in a fast and efficient manner. In this paper, we propose a new stochastic computing (SC) design to perform MUL with in-memory operations. Instead of using the stochastic number generators (SNGs), we harness the inherent stochasticity in the memory write behavior of the magnetic random access memory (MRAM). Each memory bit serves as an SC engine, performs MUL on operands in the form of write voltage pulses, and stores the MUL outcome in-situ. The proposed design provides up to 4x improvement in performance compared with conversational SC approaches, and achieves 18x speedup over implementing MUL with only in-memory bitwise Boolean logic operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
