Memristor Based TCM

Abstract

This project presents the design and synthesis of a fault-tolerant neuromorphic system using memristor-based technology. The primary objective is to develop a low-power, highly efficient hardware model that mimics biological neural behavior while maintaining robustness against faults. By integrating a TCM (Trellis Coded Modulation) encoder with a memristor-based processing unit, the system achieves improved error correction and reliability. The proposed design was implemented using Verilog HDL and synthesized through industry-standard EDA tools, providing insights into power consumption, area utilization, and timing performance. The synthesis reports demonstrate that the system operates within acceptable power limits while occupying minimal silicon area, making it ideal for resource-constrained environments. The timing analysis confirms that the design meets setup and hold requirements, ensuring stable and reliable operation. This work contributes to the growing field of neuromorphic engineering

Introduction

The document explores the development and optimization of memristor-based neuromorphic computing systems, which mimic brain-like parallelism, adaptability, and energy efficiency. Central to this technology is the memristor—a non-volatile device that emulates synaptic behavior but faces challenges like variability and faults that require fault-tolerant designs for reliability.

The study investigates fault-tolerant mechanisms, device modeling (physics-based and phenomenological), and ASIC design tailored for memristor architectures to achieve optimized performance, low power consumption, and compactness. Using hardware description languages (Verilog), the memristor modules are designed, functionally verified through simulation (ModelSim, Xilinx ISE), and synthesized using tools like Cadence Genus and Vivado.

Key methodologies include modular RTL design, thorough simulation to validate arithmetic and logical operations, and power-aware synthesis that applies optimizations like clock gating and logic restructuring to minimize power without sacrificing performance. The memristor module compresses partial products in multiplication, enhancing speed and efficiency for applications in digital signal processing and AI.

The text also details the implementation of a memristor-based Trellis Coded Modulation (TCM) encoder, verified through simulation and mapped efficiently on FPGA hardware, demonstrating robust and synchronous operation with low power consumption.

Post-synthesis reports from Cadence Genus show successful optimization in power, area, and timing, highlighting dominant dynamic power usage and confirming the design’s suitability for energy-efficient, reliable neuromorphic and embedded system applications.

Conclusion

The exploration of fault-tolerant designs in memristor-based neuromorphic systems emphasizes the critical role of reliability in emerging computing technologies. By leveraging the memristor’s ability to mimic synaptic plasticity and retain memory without power, these systems show great promise for energy-efficient, brain-inspired architectures. However, their inherent susceptibility to faults due to nanoscale variability necessitates robust design methodologies. The integration of adaptive algorithms, redundancy schemes, and fault detection mechanisms ensures continued functionality even under compromised conditions, reinforcing their applicability in real-world scenarios. Furthermore, accurate modeling and ASIC-level implementation are pivotal for translating theoretical advances into practical solutions. With modeling approaches ranging from physics-based to compact SPICE-compatible abstractions, designers can optimize both simulation efficiency and design accuracy. The use of ASIC frameworks tailored for memristor-based operations not only enhances performance and power efficiency but also demonstrates readiness for deployment in specialized domains such as robotics, edge devices, and autonomous systems. As research progresses, the combination of reliable hardware, intelligent fault management, and scalable design techniques will be essential to fully realize the potential of neuromorphic computing.

References

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Copyright

Copyright © 2025 Anusha Sowpati , Madhuri Vemuluri . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.