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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

Memory Issues For AI Edge Chips


Several companies are developing or ramping up AI chips for systems on the network edge, but vendors face a variety of challenges around process nodes and memory choices that can vary greatly from one application to the next. The network edge involves a class of products ranging from cars and drones to security cameras, smart speakers and even enterprise servers. All of these applications in... » read more

Taming Novel NVM Non-Determinism


New memory technologies may have non-deterministic characteristics that add calibration to the test burden — and may require recalibration during their lifetime. Many of these memories are in development as a result of the search for a storage-class memory (SCM) technology that can bridge the gap between larger, slower memories like flash and faster DRAM memory. There are several approache... » read more

DRAM Scaling Challenges Grow


DRAM makers are pushing into the next phase of scaling, but they are facing several challenges as the memory technology approaches its physical limit. DRAM is used for main memory in systems, and today’s most advanced devices are based on roughly 18nm to 15nm processes. The physical limit for DRAM is somewhere around 10nm. There are efforts in R&D to extend the technology, and ultimate... » read more

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