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NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark


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


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


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

Power/Performance Bits: Nov. 16


Light-emitting memory Researchers from Kyushu University and National Taiwan Normal University propose a 'light-emitting memory' based on a perovskite that can simultaneously store and visually transmit data. The team used the idea in conjunction with resistive RAM (RRAM), in which states of high and low resistance represent ones and zeros. "The electrical measurements needed to check the r... » read more

Intermittent Undefined State Fault in RRAMs


Abstract: " Industry is prototyping and commercializing Resistive Random Access Memories (RRAMs). Unfortunately, RRAM devices introduce new defects and faults. Hence, high-quality test solutions are urgently needed. Based on silicon measurements, this paper identifies a new RRAM unique fault, the Intermittent Undefined State Fault (IUSF); this fault causes the RRAM device to intermittently c... » read more

A Novel PUF Using Stochastic Short-Term Memory Time of Oxide-Based RRAM for Embedded Applications


Abstract: "RRAM suffers from poor retention with short-term memory time when using low compliance current for programing. However, the short-term memory time exhibits ideal randomness, which can be exploited as an entropy source for physically unclonable function (PUF). In this work, we demonstrated a novel PUF utilizing the stochastic short-term memory time of oxide-based RRAM. The proposed P... » read more

A Machine-Learning-Resistant 3D PUF with 8-layer Stacking Vertical RRAM and 0.014% Bit Error Rate Using In-Cell Stabilization Scheme for IoT Security Applications


Abstract: "In this work, we propose and demonstrate a multi-layer 3-dimensional (3D) vertical RRAM (VRRAM) PUF with in-cell stabilization scheme to improve both cost efficiency and reliability. An 8-layer VRRAM array was manufactured with excellent uniformity and good endurance of >10 7 . Apart from the variation in RRAM resistance, enhanced randomness is obtained thanks to the parasitic IR... » read more

Manufacturing Bits: Feb. 2


Capacitor-less DRAM At the recent 2020 International Electron Devices Meeting (IEDM), Imec presented a paper on a novel capacitor-less DRAM cell architecture. 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. DRAM itself is based on a one-transistor, one-capacito... » read more

Spiking Neural Networks: Research Projects or Commercial Products?


Spiking neural networks (SNNs) often are touted as a way to get close to the power efficiency of the brain, but there is widespread confusion about what exactly that means. In fact, there is disagreement about how the brain actually works. Some SNN implementations are less brain-like than others. Depending on whom you talk to, SNNs are either a long way away or close to commercialization. Th... » 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

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