I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The organization of behavior: A neuropsychological theory

Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Memristive devices for computing

Resistance switching in metal oxides could form the basis for next-generation non-volatile memory. It has been argued that the current in the high-conductivity state of several technologically relevant oxide materials flows through localized filaments, but these filaments have been characterized only indirectly, limiting our understanding of the switching mechanism. Here, we use high-resolution transmission electron microscopy to probe directly the nanofilaments in a Pt/TiO2/Pt system during resistive switching. In situ current–voltage and low-temperature (∼130 K) conductivity measurements confirm that switching occurs by the formation and disruption of TinO2n−1 (or so-called Magneli phase) filaments. Knowledge of the composition, structure and dimensions of these filaments will provide a foundation for unravelling the full mechanism of resistance switching in oxide thin films, and help guide research into the stability and scalability of such films for applications. Nanoscale filaments with a Magneli structure are shown to be responsible for resistance switching in thin films of TiO2, and the properties of the filaments are directly observed during the switching process.

Atomic structure of conducting nanofilaments in TiO2 resistive switching memory

We developed two-step solution-phase reactions to form hybrid materials of Mn3O4 nanoparticles on reduced graphene oxide (RGO) sheets for lithium ion battery applications. Mn3O4 nanoparticles grown selectively on RGO sheets over free particle growth in solution allowed for the electrically insulating Mn3O4 nanoparticles wired up to a current collector through the underlying conducting graphene network. The Mn3O4 nanoparticles formed on RGO show a high specific capacity up to ~900mAh/g near its theoretical capacity with good rate capability and cycling stability, owing to the intimate interactions between the graphene substrates and the Mn3O4 nanoparticles grown atop. The Mn3O4/RGO hybrid could be a promising candidate material for high-capacity, low-cost, and environmentally friendly anode for lithium ion batteries. Our growth-on-graphene approach should offer a new technique for design and synthesis of battery electrodes based on highly insulating materials.

Mn3O4-Graphene Hybrid as a High Capacity Anode Material for Lithium Ion Batteries

Emerging nonvolatile main memory (NVMM) suffers from secure vulnerability due to its nonvolatility. To address this issue, existing methods tend to employ the encryption engine on the CPU side for encryption. However, this incurs large energy and latency overhead due to the massive data movement between the CPU and NVMM. On the other hand, popular encryption algorithms like the Advanced Encryption Standard (AES) usually involve massive bit-level parallelism. As a result, an emerging technology named logic-in-memory (LiM), which leverages the electrical characteristics of nonvolatile devices to enable efficient in-memory Boolean operations in parallel, is a promising solution to eliminating data movement overhead and enables faster and more energy-efficient encryption.

Efficient In-Memory AES Encryption Implementation Using a General Memristive Logic: Surmounting the data movement bottleneck

Nanosession: Logic Devices and Circuit Design

Oxide-based resistive switching devices have tremendous potential in next-generation nonvolatile memory and neuromorphic applications. Here, the emergence of quantized conductance is investigated in resistive switching devices based on Ta 2 O 5 or HfO 2 . By applying sweeping voltages with different current compliances or using consecutive voltage pulses, quantized conductance states including integer and half integer multiples of quantum conductance (G 0 ) were observed, suggesting well-controlled formation of atomic point contacts. Compared with Pt/Ta/Ta 2 O 5 /Pt devices, a larger number of quantized conductance states were obtained in the Pt/Ta/HfO 2 /Pt devices. Such quantized conductance states are inherently discrete and multilevel, which could be promising for applications as multilevel nonvolatile memory and artificial synapses in hardware neural networks.

Conductance quantization in oxide-based resistive switching devices

The combinatorial optimization problem (COP), which aims to find the optimal solution in discrete space, is fundamental in various fields. Unfortunately, many COPs are NP-complete, and require much more time to solve as the problem scale increases. Troubled by this, researchers may prefer fast methods even if they are not exact, so approximation algorithms, heuristic algorithms, and machine learning have been proposed. Some works proposed chaotic simulated annealing (CSA) based on the Hopfield neural network and did a good job. However, CSA is not something that current general-purpose processors can handle easily, and there is no special hardware for it. To efficiently perform CSA, we propose a software and hardware co-design. In software, we quantize the weight and output using appropriate bit widths, and then modify the calculations that are not suitable for hardware implementation. In hardware, we design a specialized processing-in-memory hardware architecture named COPPER based on the memristor. COPPER is capable of efficiently running the modified quantized CSA algorithm and supporting the pipeline further acceleration. The results show that COPPER can perform CSA remarkably well in both speed and energy.

COPPER: a combinatorial optimization problem solver with processing-in-memory architecture

In this paper, the impact of forming voltage polarity on Pt/HfO 2 /TiN RRAM performance is investigated. Experimental results show that performance including forming voltage and resistance ratio can be improved by tuning the forming voltage polarity at the inert electrode. The underlying mechanism may be the suppression of the filament overgrowing by the reverse forming voltage.

Yuchao Yang

Papers

Efficient In-Memory AES Encryption Implementation Using a General Memristive Logic: Surmounting the data movement bottleneck

Nanosession: Logic Devices and Circuit Design

Conductance quantization in oxide-based resistive switching devices

COPPER: a combinatorial optimization problem solver with processing-in-memory architecture

Impact of Forming Voltage Polarity on HfO 2 -based RRAM Performance