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16.1 A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices

机译:16.1 22nm 4MB 8B-Precision Reram Computing-In-Memory宏,带11.91至195.7秒/ W for Tiny AI Edge设备

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Battery-powered tiny-AI edge devices require large-capacity nonvolatile compute-in-memory (nvCIM), with multibit input (IN), weight (W), and output (OUT) precision to support complex applications, high-energy efficiency (EF$_{{mathrm {MAC}}})$, and short computing latency $(t_{{mathrm {AC}}})$ for multiply-and-accumulate (MAC) operations. Due to the low read-disturbfree voltage of nonvolatile memory (NVM) devices and the large parasitic load on the bitline, most existing Mb-level nvCIM macros use a current-mode read scheme [1–5] and only achieve a low IN-W precision (binary to 4b).
机译:电池供电的Tiny-AI Edge设备需要大容量的非易失性计算内存(NVCIM),具有多点输入(IN),重量(W),以及输出(OUT)精度,以支持复杂的应用,高能量效率( EF $ _ {{ mathrm {mac}}})$,以及用于乘法和累积(MAC)操作的SWORT计算延迟$(t _ {{{{{ mathrm {ac}})。由于非易失性存储器(NVM)器件的低读取损坏折衷电压和位线上的大寄生载荷,大多数现有的MB级NVCIM宏使用电流模式读取方案[1-5],只能达到低W精度(二进制到4B)。

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