Analog In-Memory Cores With Multi-Memristive Unit-Cells (IBM)

A technical paper titled “Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing” was published by researchers at IBM Research-Europe, IBM Research-Albany, and IBM Research-Yorktown Heights. Abstract: "Analog in-memory computing (AIMC) using memristive devices is considered a promising Non-von Neumann approach for deep learning (DL... » read more

Research Bits: September 26

2D waveguides Researchers from the University of Chicago found that a sheet of glass crystal just a few atoms thick could trap and carry light efficiently up to a centimeter. In tests, the researchers found they could use extremely tiny prisms, lenses, and switches to guide the path of the light along a chip. “We were utterly surprised by how powerful this super-thin crystal is; not on... » read more

Optimizing Projected PCM for Analog Computing-In-Memory Inferencing (IBM)

A new technical paper titled "Optimization of Projected Phase Change Memory for Analog In-Memory Computing Inference" was published by researchers at IBM Research. "A systematic study of the electrical properties-including resistance values, memory window, resistance drift, read noise, and their impact on the accuracy of large neural networks of various types and with tens of millions of wei... » read more

Neuromorphic Computing: Challenges, Opportunities Including Materials, Algorithms, Devices & Ethics

This new research paper titled "2022 roadmap on neuromorphic computing and engineering" is from numerous researchers at Technical University of Denmark, Instituto de Microelectrónica de Sevilla, CSIC, University of Seville, and many others. Partial Abstract: "The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the chall... » read more

Neurosynaptic Device That Mimics Synaptic and Intrinsic Plasticity Concomitantly In a Single cell

New academic paper titled "Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse" from researchers at Korea Advanced Institute of Science and Technology (KAIST). Abstract Neuromorphic computing targets the hardware embodiment of neural network, and device implementation of individual neuron and synapse has attracted considerable attention. The emulation of... » read more

An Energy-Efficient DRAM Cache Architecture for Mobile Platforms With PCM-Based Main Memory

Abstract "A long battery life is a first-class design objective for mobile devices, and main memory accounts for a major portion of total energy consumption. Moreover, the energy consumption from memory is expected to increase further with ever-growing demands for bandwidth and capacity. A hybrid memory system with both DRAM and PCM can be an attractive solution to provide additional capacity ... » read more

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices

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

Power/Performance Bits: Nov. 24

Flexible, low power phase-change memory Engineers at Stanford University created a flexible phase-change memory. The non-volatile phase-change memory device is made up of germanium, antimony, and tellurium (GST) between two metal electrodes. 1s and 0s represent measurements of electrical resistance in the GST material. “A typical phase-change memory device can store two states of resis... » read more

More Data, More Memory-Scaling Problems

Memories of all types are facing pressures as demands grow for greater capacity, lower cost, faster speeds, and lower power to handle the onslaught of new data being generated daily. Whether it's well-established memory types or novel approaches, continued work is required to keep scaling moving forward as our need for memory grows at an accelerating pace. “Data is the new economy of this ... » read more

DRAM, 3D NAND Face New Challenges

It’s been a topsy-turvy period for the memory market, and it's not over. So far in 2020, demand has been slightly better than expected for the two main memory types — 3D NAND and DRAM. But now there is some uncertainty in the market amid a slowdown, inventory issues and an ongoing trade war. In addition, the 3D NAND market is moving toward a new technology generation, but some are enc... » read more

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