Yao-Feng Chang

By Cheng-Chih Hsieh, Yao-Feng Chang, Ying-Chen Chen, Xiaohan Wu, Meiqi Guo, Fei Zhou, Sungjun Kim, Burt Fowler, Chih-Yang Lin, Chih-Hung Pan, Ting-Chang Chang and Jack C. Lee

In this chapter, we focus on the recent process on memcomputing (memristor + computing) in intrinsic SiOx-based resistive switching memory (ReRAM or called memristor). In the first section of the chapter, we investigate neuromorphic computing by mimicking the synaptic behaviors in integrating one-diode and one-resistive switching element (1D-1R) architecture. The power consumption can be minimized further in synaptic functions because sneak-path current has been suppressed and the capability for spike-induced synaptic behaviors has been demonstrated, representing critical milestones and achievements for the application of conventional SiOx-based materials in future advanced neuromorphic computing. In the next section of chapter, we will discuss an implementation technique of implication operations for logic-in-memory computation by using a SiOx-based memristor. The implication function and its truth table have been implemented with the unipolar or nonpolar operation scheme. Furthermore, a circuit with 1D-1R architecture with a 4 × 4 crossbar array has been demonstrated, which realizes the functionality of a one-bit full adder as same as CMOS logic circuits with lower design area requirement. This chapter suggests that a simple, robust approach to realize memcomputing chips is quite compatible with large-scale CMOS manufacturing technology by using an intrinsic SiOx-based memristor.

Part of the book: Memristor and Memristive Neural Networks

By Amirali Amirsoleimani, Tony Liu, Fabien Alibart, Serge Eccofey, Yao-Feng Chang, Dominique Drouin and Roman Genov

In this Chapter, we review the recent progress on resistance drift mitigation techniques for resistive switching memory devices (specifically memristors) and its impact on the accuracy in deep neural network applications. In the first section of the chapter, we investigate the importance of soft errors and their detrimental impact on memristor-based vector–matrix multiplication (VMM) platforms performance specially the memristance state-drift induced by long-term recurring inference operations with sub-threshold stress voltage. Also, we briefly review some currently developed state-drift mitigation methods. In the next section of the chapter, we will discuss an adaptive inference technique with low hardware overhead to mitigate the memristance drift in memristive VMM platform by using optimization techniques to adjust the inference voltage characteristic associated with different network layers. Also, we present simulation results and performance improvements achieved by applying the proposed inference technique by considering non-idealities for various deep network applications on memristor crossbar arrays. This chapter suggests that a simple low overhead inference technique can revive the functionality, enhance the performance of memristor-based VMM arrays and significantly increases their lifetime which can be a very important factor toward making this technology as a main stream player in future in-memory computing platforms.

Part of the book: Memristor

By Muhammad Ahsan Kaleem, Jack Cai, Yao-Feng Chang, Roman Genov and Amirali Amirsoleimani

One of the main issues that memristors face, like other hardware components, is non-idealities (that can arise from long-term usage, low-quality hardware, etc.). In this chapter, we discuss some ways of mitigating the effects of such non-idealities. We consider both hardware-based solutions and universal solutions that do not depend on hardware or specific types of non-idealities, specifically in the context of memristive neural networks. We compare such solutions both theoretically and empirically using simulations. We also explore the different non-idealities in depth, such as device faults, endurance, retention, and finite conductance states, considering what causes them and how they can be avoided, and present ways of simulating these non-idealities in software.

Part of the book: Memristors