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With the prosperous development of Deep Neural Network (DNNs), numerous Process-In-Memory (PIM) designs have emerged to accelerate DNN models with exceptional throughput and energy-efficiency. PIM accelerators based on Non-Volatile Memory (NVM) or volatile memory offer distinct advantages for computational efficiency and performance. NVM based PIM accelerators, demonstrated success in DNN inference, face limitations in on-device learning due to high write energy, latency, and instability. Conversely, fast volatile memories, like SRAM, offer rapid read/write operations for DNN training, but suffer from significant leakage currents and large memory footprints. In this paper, for the first time, we present a fully-digital sparse processing in hybrid NVM-SRAM design, synergistically combines the strengths of NVM and SRAM, tailored for on-device continual learning. Our designed NVM and SRAM based PIM circuit macros could support both storage and processing of N:M structured sparsity pattern, significantly improving the storage and computing efficiency. Exhaustive experiments demonstrate that our hybrid system effectively reduces area and power consumption while maintaining high accuracy, offering a scalable and versatile solution for on-device continual learning. 

While magnetoresistive random-access memory (MRAM) stands out as a leading candidate for embedded nonvolatile memory and last-level cache applications, its endurance is compromised by substantial self-heating due to the high programming current density. The effect of self-heating on the endurance of the magnetic tunnel junction (MTJ) has primarily been studied in spin-transfer torque (STT)-MRAM. Here, we analyze the transient temperature response of two-terminal spin–orbit torque (SOT)-MRAM with a 1 ns switching current pulse using electro-thermal simulations. We estimate a peak temperature range of 350–450 °C in 40 nm diameter MTJs, underscoring the critical need for thermal management to improve endurance. We suggest several thermal engineering strategies to reduce the peak temperature by up to 120 °C in such devices, which could improve their endurance by at least a factor of 1000× at 0.75 V operating voltage. These results suggest that two-terminal SOT-MRAM could significantly outperform conventional STT-MRAM in terms of endurance, substantially benefiting from thermal engineering. These insights are pivotal for thermal optimization strategies in the development of MRAM technologies.