UC Santa Cruz

Data-intensive storage and computing systems call for continuing advancements both in data latency and energy efficiency. However, the major benefits from CMOS technologies, such as high packing density and processing speed, are getting desensitized primarily due to the prohibitively increasing power density. In order for the next-generation storage and computing systems to be capable of high performance data-intensive applications, it is necessary to continue innovations in creating new circuits and system architectures, together with searching for new materials and devices.

The memristor, short for memory resistor, is a two-terminal passive device whose resistance is controlled by external electrical signals and exhibits non-volatile memory function. Due to its capabilities of non-volatile resistive memories, nanoscale miniaturization in an ultra-high packing density, and intriguing nonlinear dynamics, the memristor is being widely investigated to create new advanced circuit functions and to complement CMOS systems. The memristor technologies will lead current CMOS-based storage and computing systems to the data-intensive electronic systems, with significantly reduced stand-by power, form factor and manufacturing cost.

Developing compact models for the memristor is essential to facilitate circuit analyses and designs with memristors. While the previously reported memristor models exhibit limitations in the model stability, versatility and adaptability, we propose a new module-based memristor model that covers a wide range of device behaviors. Coincident to the theoretic memristor behaviors, the proposed model uniquely reveals that an effective charge-flux constitutive relationship can always be obtained from various types of memristors. The stability of the proposed model is also significantly enhanced by adapting the new charge (or flux)-based window function.

Associative lookup functions with high throughputs are widely implemented in Ternary Content Addressable Memories (TCAMs). The TCAM holds the potential to curb the latency and power requirements of data-intensive systems. However existing TCAMs that commonly utilize Static Random Access Memories (SRAMs) as the storage units exhibit low storage capacity/density and high cost-per-bit due to bit cells with large areas. We propose a memristor-based ternary content addressable memory (mTCAM) for data-intensive applications. A novel bit cell structure is presented that not only minimizes the bit cell area but also is capable of performance optimizations on the latency and energy consumption. Detailed design issues such as voltage compliance to ensure correct write/search operations, parameter-dependent sensing margins and device variations are also discussed. Circuit level simulations have demonstrated functionalities of the mTCAM. Performance evaluation has shown that mTCAM achieves impressive storage density, search latency and energy consumption. The proposed mTCAM is an attractive candidate in building future computing systems for data-intensive applications.