Short-Term Long-Term Compute-in-Memory Architecture: A Hybrid Spin/CMOS Approach Supporting Intrinsic Consolidation

Abstract

Biological memory structures impart enormous retention capacity while automatically providing vital functions for chronological information management and update the resolution of the domain and episodic knowledge. A crucial requirement for hardware realization of such cortical operations found in biology is to first design both short-term memory (STM) and long-term memory (LTM). Herein, these memory features are realized via a beyond-CMOS-based learning approach derived from the repeated input information and retrieval of the encoded data. We first propose a new binary STM-LTM architecture with composite synapse of the spin Hall effect-driven magnetic tunnel junction (SHE-MTJ) and capacitive memory bit cell to mimic the behavior of biological synapses. This STM-LTM platform realizes the memory potentiation through a continual update process using STM-to-LTM transfer, which is applied to neural networks based on the established capacitive crossbar. We then propose a hardware-enabled and customized STM-LTM transition algorithm for the platform considering the real hardware parameters. We validate the functionality of the design using SPICE simulations that show the proposed synapse has the potential of reaching ~30.2-pJ energy consumption for STM-to-LTM transfer and 65 pJ during STM programming. We further analyze the correlation between energy, array size, and STM-to-LTM threshold utilizing the MNIST data set.

Keywords

• Beyond-CMOS devices
• capacitive crossbar
• compute in memory
• long-term short-term memory (LTM-STM)
• spin Hall effect-driven magnetic tunnel junction (SHE-MTJ)