Many search engines or filters for the internet-of-things and big-data employ ternary content-addressable-memory (TCAM) to suppress power consumption in the transmission of data between end-devices and servers. Nonvolatile TCAMs (nvTCAM) are designed to achieve zero standby power with smaller area overhead and faster power off/on operations than those found in conventional TCAM+NVM 2-macro schemes. In this paper, we discuss the challenges involved in the design of nvTCAMs and propose a universal 5T2R nvTCAM cell with tolerance for the various R-ratios and write parameters associated with emerging memory devices. A 128×64b-nvTCAM macro was fabricated using HfO ReRAM and a 90nm-CMOS process for concept verification.

NVM has commonly been used to address increasingly large last-level caches (LLCs) requirements by reducing leakage. However, frequent data-writing operations result in increased energy consumption. In this context, a promising memory technology, Non-volatile SRAM (nvSRAM), enables normal and standby operation modes which can be used to store various types of data. However, nvSRAM suffers from high dynamic energy usage due to frequent switching between operation modes. In this paper, we propose a redundant store elimination (RSE) scheme which, on average, discards 94% of needless bit-write operations. Moreover, we present a retention-aware cache management policy to reduce data updates of cache blocks, based on the correlation between data lifetime and cache types. Experimental results demonstrate that our proposal can improve energy consumption of SRAM-based and RRAM-based LLCs by 57% and 31%, respectively.

Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total‐Community‐Purity and Total‐Cluster‐Purity both at approximately 0.8.

The use of low voltage circuits and power-off mode help to reduce the power consumption of chips. Non-volatile logic (nvLogic) and nonvolatile SRAM (nvSRAM) enable a chip to preserve its key local states and data, while providing faster power-on/off speeds than those available with conventional two-macro schemes. Resistive memory (memristor) devices feature fast write speed and low write power. Applying memristors to nvLogic and nvSRAMs not only enables chips to achieve low power consumption for store operations, but also achieve fast power-on/off processes and reliable operation even in the event of sudden power failure. However, current memristor devices suffer from limited endurance, which influences the design of the circuit structure for memristor-based nvLogic and nvSRAM. Moreover, previous nvLogic/nvSRAM circuits cannot achieve low voltage operation. This paper explores various circuit structures for nvLogic and nvSRAM, taking into account memristor endurance, especially for low-voltage applications.