tag: RRAM

Technical Paper Round-Up: July 5
By Linda Christensen - 05 Jul, 2022 - Comments: 0

New technical papers added to Semiconductor Engineering’s library this week. [table id=36 /] Semiconductor Engineering is in the process of building this library of research papers. Please send suggestions (via comments section below) for what else you’d like us to incorporate. If you have research papers you are trying to promote, we will review them to see if they are a good fit for... » read more

MEMprop: Gradient-based Learning To Train Fully Memristive SNNs
By Technical Paper Link - 04 Jul, 2022 - Comments: 0

New technical paper titled "Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays" from IEEE researchers. Abstract "We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger naturally arising voltage spikes. These spikes emitted by memristive dynamics are anal... » read more

End-to-End System for Object Localization By Coupling pMUTs to a Neuromorphic RRAM-based Computational Map
By Technical Paper Link - 20 Jun, 2022 - Comments: 0

New research paper titled "Neuromorphic object localization using resistive memories and ultrasonic transducers" from researchers at CEA, LETI, Université Grenoble Alpes and others. Abstract "Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary... » read more

Using Dynamic Route Map Technique for Insight Into Memristors
By Technical Paper Link - 24 May, 2022 - Comments: 0

New technical paper titled "Empirical Characterization of ReRAM Devices Using Memory Maps and a Dynamic Route Map," from Balearic Islands University, UC Berkeley, Health Institute of the Balearic Islands, International Hellenic University, Technische Universität Dresden, Universidad de Valladolid, and Aristotle University of Thessaloniki. Abstract: "Memristors were proposed in the early 1... » read more

Data-driven RRAM device models using Kriging interpolation
By Technical Paper Link - 10 Apr, 2022 - Comments: 0

New technical paper from The George Washington University and NIST with support from DARPA and others. Abstract "A two-tier Kriging interpolation approach is proposed to model jump tables for resistive switches. Originally developed for mining and geostatistics, its locality of the calculation makes this approach particularly powerful for modeling electronic devices with complex behavior la... » read more

CXL and OMI: Competing or Complementary?
By Bryon Moyer - 23 Mar, 2022 - Comments: 1

System designers are looking at any ideas they can find to increase memory bandwidth and capacity, focusing on everything from improvements in memory to new types of memory. But higher-level architectural changes can help to fulfill both needs, even as memory types are abstracted away from CPUs. Two new protocols are helping to make this possible, CXL and OMI. But there is a looming question... » read more

A Framework For Ultra Low-Power Hardware Accelerators Using NNs For Embedded Time Series Classification
By Fraunhofer IIS/EAS - 09 Mar, 2022 - Comments: 0

In embedded applications that use neural networks (NNs) for classification tasks, it is important to not only minimize the power consumption of the NN calculation, but of the whole system. Optimization approaches for individual parts exist, such as quantization of the NN or analog calculation of arithmetic operations. However, there is no holistic approach for a complete embedded system design ... » read more

NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark
By Technical Paper Link - 20 Dec, 2021 - Comments: 0

Abstract:   "Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardw... » read more

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices
By Technical Paper Link - 20 Dec, 2021 - Comments: 0

Abstract:  "Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate i... » read more

All-inorganic perovskite quantum dot light-emitting memories
By Technical Paper Link - 18 Nov, 2021 - Comments: 0

Abstract "Field-induced ionic motions in all-inorganic CsPbBr3 perovskite quantum dots (QDs) strongly dictate not only their electro-optical characteristics but also the ultimate optoelectronic device performance. Here, we show that the functionality of a single Ag/CsPbBr3/ITO device can be actively switched on a sub-millisecond scale from a resistive random-access memory (RRAM) to a light-e... » read more

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