In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives

International audienceMining big data to make predictions or decisions is the main goal of modern artificial intelligence (AI) and machine learning (ML) applications. Vast innovation in algorithms, their software implementations and data management has enabled great progress to date, but wide adoption has been slowed by limited capabilities of existing computing hardware. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing (e.g., in GPUs) help alleviate the data communication bottleneck to some extent, but paradigm-shifting concepts are required. In-memory computing has emerged as a prime candidate to eliminate this bottleneck by co-locating the memory and processing. In this context, resistive switching (RS) memory devices is a key promising choice, due to their unique intrinsic device-level properties enabling both storing and computing with a small, massively-parallel footprint at a low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. We present a qualitative and quantitative analysis of several key existing challenges in implementing high-capacity, high-volume RS memories for accelerating the most computationally demanding computation in ML inference – that of vector-matrix multiplication (VMM). Monolithic integration of RS memories with CMOS integrated circuits is presented as the core underlying technology. We review key existing design choices in terms of device-level physical implementation, circuit-level design, and system-level considerations, and provide an outlook for future directions

Liquid phase determination of adrenaline uses a voltammetric sensor employing CuFe2O4 nanoparticles and room temperature ionic liquids

A highly sensitive and quick voltammetric sensor based on CuFe2O4 nanoparticle/room temperature ionic liquids carbon paste electrode (CuFe2O4/ILs/CPE) is proposed for the determination of adrenaline in the biological condition (pH = 7.4). CuFe2O4 are nanoparticles synthesized by co-precipitation method and characterized with different methods such as transmission electron microscopy (TEM) and X-ray diffraction (XRD). In continuous, we study application of the CuFe2O4 nanoparticle for the preparation of carbon paste electrode modified with 1,3-dipropylimidazolium bromide as a suitable and high conductive binder. At the best condition (pH 7.4), the peak currents of square wave voltammograms (SWV) of adrenaline increased linearly with its concentration in the ranges of 0.1-400 μM. The detection limit for adrenaline was 0.07 μM. The modified sensor has been successfully applied for the assay of adrenaline in biological and pharmaceutical samples such as urine and injection in comparison with other method. © 2015 Elsevier B.V. All rights reserved