9 A Flash-SRAM-ADC-Fused Plastic Computing-in-Memory Macro for Learning in Neural Networks in a Standard 14nm FinFET Process

AI edge devices are not only required to perform inference tasks with low power and high real-time performance but are also expected to have the capability to learn and adapt to dynamic and unpredictable environments, without heavily relying on cloud-based training. The recent rise of computing-in-memory (CIM) has offered a competent solution by minimizing the power and latency associated with data movement. While many existing CIM designs 1–6 have primarily focused on improving the performance of AI inference, those with learning abilities have, so far, been relatively less studied.