一个超紧凑的单FeFET二进制和多位关联搜索引擎
An Ultra-Compact Single FeFET Binary and Multi-Bit Associative Search Engine
摘要：
内容可寻址存储器（CAM）因其高度并行的模式匹配能力而被广泛应用于关联搜索任务中。为了适应日益复杂和数据密集型的模式匹配任务，关键是不断提高CAM密度，以提高性能和面积效率。在这项工作中，我们展示了：i）一种新的超紧凑的1FeFET CAM设计，使并行关联搜索和内存中的汉明距离计算; ii）一个多位CAM精确搜索使用相同的CAM单元; iii）紧凑的设备设计，集成串联电阻电流限制器到本征FeFET结构，把1FeFET 1 R变成一个有效的1FeFET单元; iv）在给定现有未优化FeFET器件变化的情况下，在实验和模拟中具有实际感兴趣的尺寸的所提出的二进制和多位1FeFET 1 R CAM阵列的成功的2步搜索操作和足够的感测裕度; v）与GPU上最先进的比对工具相比，在通过超维计算范例加速基因组模式匹配应用方面，加速比为89.9倍，能效提高66.5倍。
英文摘要：
Content addressable memory (CAM) is widely used in associative search tasks for its highly parallel pattern matching capability. To accommodate the increasingly complex and data-intensive pattern matching tasks, it is critical to keep improving the CAM density to enhance the performance and area efficiency. In this work, we demonstrate: i) a novel ultra-compact 1FeFET CAM design that enables parallel associative search and in-memory hamming distance calculation; ii) a multi-bit CAM for exact search using the same CAM cell; iii) compact device designs that integrate the series resistor current limiter into the intrinsic FeFET structure to turn the 1FeFET1R into an effective 1FeFET cell; iv) a successful 2-step search operation and a sufficient sensing margin of the proposed binary and multi-bit 1FeFET1R CAM array with sizes of practical interests in both experiments and simulations, given the existing unoptimized FeFET device variation; v) 89.9x speedup and 66.5x energy efficiency improvement over the state-of-the art alignment tools on GPU in accelerating genome pattern matching applications through the hyperdimensional computing paradigm.
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