Abstract

Low-power Al edge devices should provide short-latency <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathsf{T}_{\mathsf{WK-RP}})$</tex> and low-energy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathsf{E}_{\mathsf{WK-RP}})$</tex> wakeup responses from power-off mode to handle event-triggered computing tasks with high inference accuracy (IA), which requires high-capacity nonvolatile memory (NVM) to store high-precision weight data in power-off and high bit-precision multiply and accumulate (MAC) operations with high energy efficiency. SRAM computing-in-memory (CIM) and digital processors suffer large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{E}_{\mathsf{WK-RP}}$</tex> and long <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{T}_{\mathsf{WK-RP}}$</tex> due to the movement of weight data from off-chip NVM to the on-chip buffer and processing unit after wakeup. Thus, on-chip nonvolatile CIM (nvCIM) is preferred for Al-edge processors by combining NVM storage and computing tasks on the same macro. Among nvCIM structures, in-memory compute (IMC) [1] provides short computing latency and high energy efficiency, but suffers from low computing yield. Near-memory compute (NMC) [2–4] provides high computing yield, but suffers long computing latency and low energy efficiency.

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