34.9 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.