COSMO: Computing with Stochastic Numbers in Memory

Stochastic computing (SC) reduces the complexity of computation by representing numbers with long streams of independent bits. However, increasing performance in SC comes with either an increase in area or a loss in accuracy. Processing in memory (PIM) computes data in-place while having high memory density and supporting bit-parallel operations with low energy consumption. In this article, we propose COSMO, an architecture for co mputing with s tochastic numbers in me mo ry, which enables SC in memory. The proposed architecture is general and can be used for a wide range of applications. It is a highly dense and parallel architecture that supports most SC encodings and operations in memory. It maximizes the performance and energy efficiency of SC by introducing several innovations: (i) in-memory parallel stochastic number generation, (ii) efficient implication-based logic in memory, (iii) novel memory bit line segmenting, (iv) a new memory-compatible SC addition operation, and (v) enabling flexible block allocation. To show the generality and efficiency of our stochastic architecture, we implement image processing, deep neural networks (DNNs), and hyperdimensional (HD) computing on the proposed hardware. Our evaluations show that running DNN inference on COSMO is 141× faster and 80× more energy efficient as compared to GPU.

Integration technologies for chips with 2D materials

With compact footprint, low energy consumption, high scalability, and mass producibility, chip-scale integrated devices are an indispensable part of modern technological change and development. Recent advances in two-dimensional (2D) layered materials with their unique structures and distinctive properties have motivated their on-chip integration, yielding a variety of functional devices with superior performance and new features. To realize integrated devices incorporating 2D materials, it requires a diverse range of device fabrication techniques, which are of fundamental importance to achieve good performance and high reproducibility. This paper reviews the state-of-art fabrication techniques for the on-chip integration of 2D materials. First, an overview of the material properties and on-chip applications of 2D materials is provided. Second, different approaches used for integrating 2D materials on chips are comprehensively reviewed, which are categorized into material synthesis, on-chip transfer, film patterning, and property tuning / modification. Third, the methods for integrating 2D van der Waals heterostructures are also discussed and summarized. Finally, the current challenges and future perspectives are highlighted.

Activation and catalytic transformation of methane under mild conditions

In the last few decades, scientists have been motivated by promising production of chemicals from methane under mild conditions for low energy consumption and climate remediation; significant fundamental understanding on this topic has been achieved.

Migration-assisted, moisture gradient process for ultrafast, continuous CO2 capture from dilute sources at ambient conditions

An ultrafast, continuous CO2 capture process driven by moisture gradient and electric field with low energy consumption to capture and concentrate CO2 from dilute sources.

Exploring carbon and nitrogen removal capacity of membrane aerated biofilm reactor for low-strength municipal wastewater treatment

As one stage process capable of simultaneous carbon and nitrogen removal, membrane aerated biofilm reactor (MABR) has advantages of low energy consumption from bubble-free aeration and no extra carbon dosage...

Prediction of Nickel Removal using Diffusion Model in Flat Sheet Supported Liquid Membrane

Supported liquid membrane (SLM) is one of the potential extraction methods for the treatment of wastewater containing various toxic heavy metal ions. Advantageously, this process offers simultaneous removal and recovery, as well as low energy consumption and operational cost. In this study, the prediction of nickel removal was investigated using a diffusion model developed through MATLAB.

Wireless Resource Management and Resilience Optimization of the M2M-Oriented Mobile Communication System

Machine-to-machine (M2M) communication technology is an emerging technology that can connect smart wireless devices. The most obvious feature of M2M is that the communication between devices does not require human intervention. Therefore, ensuring the low-energy consumption of M2M devices is a necessary condition for prolonging the survival time of their devices. This paper first considers the coexistence of M2M and H2H scenarios. Aimed at the energy efficiency of M2M equipment and the channel capacity of H2H equipment, a multiobjective problem is constructed for joint spectrum and power resource management, and a weighted Chebyshev algorithm is proposed to solve this problem. Secondly, in view of the additional interference problems caused by the introduction of M2M communication, the intercell cooperative link selection algorithm is used to optimize its resilience. The effectiveness of the algorithm is proven by simulation results.