부산대학교 도서관

Memristive crossbar arrays have become essential building blocks in the realization of large-scale neuromorphic systems with high-density synaptic connectivity. Traditionally, memristor-based accelerators are equipped with selector elements to reduce cross-talk through sneak paths along unselected lines. However, due to the large drive strength required for selector elements, it comes at the cost of synaptic crossbar density. Selector- less alternatives require careful design of crossbar peripheral circuits to mitigate or eliminate sneak path-induced cross-talk. We propose a hybrid integrated platform that interfaces a selector- less memristor crossbar array with peripheral row and column instrumentation for array-parallel programming and readout for AI learning and inference applications. The proposed switched-capacitor voltage-sensing instrumentation avoids the need for current-sensing schemes with voltage-clamped sense lines that are typically used to mitigate the sneak path issues in selector- less crossbars but are substantially less energy-efficient than voltage-sensing. Our board-level platform is implemented using commercial off-the-shelf (COTS) data converters and switched capacitors, and is controlled by a Xilinx Spartan-6 FPGA. The system offers programmable sense times to characterize memristors over a wide range of resistances and the capability to switch between a transient-domain measurement and steady-state measurement to offer the desired trade-off between accuracy and energy efficiency during inference parallel readout. We implement a differential weight-encoding scheme to improve the accuracy of matrix-vector multiplication. The system also supports an array-level programming scheme for parallel write access as well as online learning-in-memory for neuromorphic applications through outer-product incremental decomposition of the weight matrix. Thus, our system offers a generic, user-configurable, and versatile platform to support wide dynamic range measurements of synaptic crossbar arrays and cognitive neuromorphic computing with emerging non-volatile memory devices.