Matthew Marinella | Searcharxiv

arXiv2026

Strong radial electric field scaling near nanoscale conductive filaments and the ReRAM resistive switching mechanism

The physics underlying reset in bipolar resistive memory has been the subject of decades of controversy and has been identified as the primary barrier to resistive memory technology development. This manuscript introduces a nanoscale effect in current carrying conductors, whereby surface charge induced radial electric fields are found to be inversely proportional to the radius of the conductive path. This nanoscale effect is then applied to explain the negative resistance switching (reset) mechanism in filamentary metal oxide resistive switching memory devices (memristors). Previous explanations for the negative resistive switching mechanism state that diffusion constitutes the radial driving mechanism for oxygen ions, and drift under electric fields is restricted to the direction parallel to current flow. This explanation conflicts with retention and microscopy data collected in a subset of devices presented in literature. We demonstrate that the electric field's dependency on the on the radius of a nanoscale conductive path can result in radial fields on the order of 10^5 to 10^6 V/cm at only -1 V bias, sufficient to govern the negative resistance switching mechanism in filamentary metal oxides. By accounting for this nanoscale size effect, long standing anomalous experimental data about the negative (reset) resistance switching mechanism in bipolar filamentary resistive memory devices is finally reconciled. Wide understanding of surface charges and associated electric fields in nanoscale conductive paths could prove important for further scaling of integrated circuits and aid in elucidating many nanoscale phenomena.

cond-mat.mes-hall↗

arXiv2019

PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM

The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Numerous special-purpose architectures have been proposed to accelerate training: both digital and hybrid digital-analog using resistive RAM (ReRAM) crossbars. ReRAM-based accelerators have demonstrated the effectiveness of ReRAM crossbars at performing matrix-vector multiplication operations that are prevalent in training. However, they still suffer from inefficiency due to the use of serial reads and writes for performing the weight gradient and update step. A few works have demonstrated the possibility of performing outer products in crossbars, which can be used to realize the weight gradient and update step without the use of serial reads and writes. However, these works have been limited to low precision operations which are not sufficient for typical training workloads. Moreover, they have been confined to a limited set of training algorithms for fully-connected layers only. To address these limitations, we propose a bit-slicing technique for enhancing the precision of ReRAM-based outer products, which is substantially different from bit-slicing for matrix-vector multiplication only. We incorporate this technique into a crossbar architecture with three variants catered to different training algorithms. To evaluate our design on different types of layers in neural networks (fully-connected, convolutional, etc.) and training algorithms, we develop PANTHER, an ISA-programmable training accelerator with compiler support. Our evaluation shows that PANTHER achieves up to $8.02\times$, $54.21\times$, and $103\times$ energy reductions as well as $7.16\times$, $4.02\times$, and $16\times$ execution time reductions compared to digital accelerators, ReRAM-based accelerators, and GPUs, respectively.

cs.DC↗