FPGA-Based Memristor Emulator Circuit for Binary Convolutional Neural Networks

Abstract

Binary convolutional neural networks (BCNN) have been proposed in the literature for resource-constrained IoTs nodes and mobile computing devices. Such computing platforms have strict constraints on the power budget, system performance, processing and memory capabilities. Nonetheless, the platforms are still required to efficiently perform classification and matching tasks needed in various applications. The memristor device has shown promising results when utilized for in-memory computing architectures, due to its ability to perform storage and computation using the same physical element. This is in addition to their nonlinear dynamic characteristics that can be used for various applications. Emulating the memristor operation through FPGAs implementation provides flexibility for rapid prototyping and exploration of the design space. In this paper, a FPGA-based memristor emulator circuit is implemented using a multi-bit XNOR gate as the main block for binary convolution which is followed by a memristorbased pooling layer. The BCNN layer is realized by bitwise XNOR-cell followed by wide NOR gate. The implementation has been successfully synthesized and verified using Xilinx Nexys4 FPGA with less than 1% utilization and 144.9 MHz frequency of operation. In order to test the functionality of the proposed cores, a digital implementation for the circuit was realized and verified experimentally. The experimental results show similar performance when compared to software simulation.

Reconfigurable multivalued memristor FPGA model for digital recognition

“…Digital emulators also attract many researchers due to their small area, hardware flexibility, reconfigurability, and ease of programming and control. [10][11][12][13][14][15][16][17] Few schemes have been proposed for low-power, reconfigurable memristor models in the literature. In Tolba et al, 12 two discrete and continuous versatile memristor models alongside their FPGA realizations are proposed.…”

“…The analog circuit emulator builds modeling equations based on the memristor's experimental data, which match the physical properties of the memristor. Digital emulators also attract many researchers due to their small area, hardware flexibility, reconfigurability, and ease of programming and control 10–17 …”

“…In Tolba et al, 13 a versatile digital memristor emulator that exhibits either continuous or discrete behaviors is proposed, similar to valence change memories (VCMs) and the electrochemical metallization memories. In Tolba et al, 14 a binary convolutional neural network was implemented based on the model proposed in another paper by Tolba et al 12 In Ntinas et al, 15 a behavioral‐level digital model of voltage‐controlled threshold bipolar memristor based on FPGA is proposed. In Eshraghian et al, 16 a behavioral model of digital resistive switching devices is proposed for neural network acceleration.…”