Integration of Ag-CBRAM crossbars and Mott ReLU neurons for efficient implementation of deep neural networks in hardware
Abstract
In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 29, 2023
- Source ID
- 10.1088/2634-4386/aceea9
Entities
People
- Duygu Kuzum
- Ivan K. Schuller
- Jaeseoung Park
- Javier del Valle
- Pavel Salev
- Sangheon Oh
- Yuhan Shi
Organizations
- National Institutes of Health
- National Science Foundation
- Office of Naval Research Global
- United States Department of Energy