Reprint
Emerging Memory and Computing Devices in the Era of Intelligent Machines
Edited by
April 2020
276 pages
- ISBN978-3-03928-502-0 (Paperback)
- ISBN978-3-03928-503-7 (PDF)
This is a Reprint of the Special Issue Emerging Memory and Computing Devices in the Era of Intelligent Machines that was published in
Chemistry & Materials Science
Engineering
Physical Sciences
Summary
Computing systems are undergoing a transformation from logic-centric towards memory-centric architectures, where overall performance and energy efficiency at the system level are determined by the density, performance, functionality and efficiency of the memory, rather than the logic sub-system. This is driven by the requirements of data-intensive applications in artificial intelligence, autonomous systems, and edge computing. We are at an exciting time in the semiconductor industry where several innovative device and technology concepts are being developed to respond to these demands, and capture shares of the fast growing market for AI-related hardware. This special issue is devoted to highlighting, discussing and presenting the latest advancements in this area, drawing on the best work on emerging memory devices including magnetic, resistive, phase change, and other types of memory. The special issue is interested in work that presents concepts, ideas, and recent progress ranging from materials, to memory devices, physics of switching mechanisms, circuits, and system applications, as well as progress in modeling and design tools. Contributions that bridge across several of these layers are especially encouraged.
Format
- Paperback
License and Copyright
© 2020 by the authors; CC BY-NC-ND license
Keywords
3D-stacked; DRAM; in-DRAM cache; low-latency; low-power; resistive memory; crossbar; in-memory computing; analogue computing; matrix-vector multiplication; ECG; voltage-controlled magnetic anisotropy; magnetoresistive random access memory; magnetic tunnel junction; bioelectronic devices; bionanohybrid material; biomemory; biologic gate; bioprocessor; protein; nucleic acid; nanoparticles; SONOS; flash memory; charge spreading; plasma treatment; Oxygen-related trap; data retention; BCH; decoder; iBM; GPU; hybrid; flash memory; Galois field; CUDA; in-memory computing; logic-in-memory; non-von Neumann architecture; configurable logic-in-memory architecture; memory wall; convolutional neural networks; emerging technologies; perpendicular Nano Magnetic Logic (pNML); silicon oxide-based memristors; resistance switching mechanism; variability; conductive filament; Weibull distribution; quantum point contact; real-time system; dynamic voltage scaling; task placement; low-power technique; nonvolatile memory; neuromorphic system; Hebbian training; guide training; memristor; image classification; STT-MRAM; flip-flop; power gating; low-power; bipolar resistive switching characteristics; annealing temperatures; solution-based dielectric; resistive random access memory (RRAM); multi-level cell; phase change memory; programmable ramp-down current pulses; Fast Fourier Transform; in-memory computing; associative processor; non-von neumann architecture; in-memory computing; memristor; RISC-V; Internet of things; blockchain; U-shape recessed channel; floating gate; neuromorphic computing; MCU (microprogrammed control unit); chalcogenide; electrochemical metallization cell; electrochemical metallization (ECM); ion conduction; memristor; self-directed channel (SDC); memristor; crossbar array; wire resistance; synaptic weight; character recognition; n/a