tag: neuromorphic computing

Energy-efficient memcapacitor devices for neuromorphic computing
By Technical Paper Link - 03 Nov, 2021 - Comments: 0

Abstract Data-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that ... » read more

Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis
By Technical Paper Link - 06 Oct, 2021 - Comments: 0

Abstract "Resistance switching in metal–insulator–metal structures has been extensively studied in recent years for use as synaptic elements for neuromorphic computing and as nonvolatile memory elements. However, high switching power requirements, device variabilities, and considerable trade-offs between low operating voltages, high on/off ratios, and low leakage have limited their utility... » read more

Making Sense Of New Edge-Inference Architectures
By Bryon Moyer - 04 Mar, 2021 - Comments: 0

New edge-inference machine-learning architectures have been arriving at an astounding rate over the last year. Making sense of them all is a challenge. To begin with, not all ML architectures are alike. One of the complicating factors in understanding the different machine-learning architectures is the nomenclature used to describe them. You’ll see terms like “sea-of-MACs,” “systolic... » read more

FeFETs Bring Promise And Challenges
By Bryon Moyer - 17 Feb, 2021 - Comments: 3

Ferroelectric FETs (FeFETs) and memory (FeRAM) are generating high levels of interest in the research community. Based on a physical mechanism that hasn’t yet been commercially exploited, they join the other interesting new physics ideas that are in various stages of commercialization. “FeRAM is very promising, but it's like all promising memory technologies — it takes a while to get b... » read more

Power/Performance Bits: Dec. 7
By Jesse Allen - 07 Dec, 2020 - Comments: 0

Logic-in-memory with MoS2 Engineers at École Polytechnique Fédérale de Lausanne (EPFL) built a logic-in-memory device using molybdenum disulfide (MoS2) as the channel material. MoS2 is a three-atom-thick 2D material and excellent semiconductor. The new chip is based on floating-gate field-effect transistors (FGFETs) that can hold electric charges for long periods. MoS2 is particularly se... » read more

Cerfe Labs: Spin-On Memory
By Ed Sperling - 05 Oct, 2020 - Comments: 0

Arm has spun off one of its more intriguing semiconductor research projects, a new non-volatile memory type called correlated electron materials RAM (CeRAM) that holds the potential to substantially reduce the cost of memory in everything from edge devices to high-performance computing. Headed by two former Arm Research insiders — Eric Hennenhoefer, who will serve as CEO and Greg Yeric, wh... » read more

Neuromorphic Computing Drives The Landscape Of Emerging Memories For Artificial Intelligence SoCs
By Synopsys - 03 Jun, 2020 - Comments: 0

The pace of deep machine learning and artificial intelligence (AI) is changing the world of computing at all levels of hardware architecture, software, chip manufacturing, and system packaging. Two major developments have opened the doors to implementing new techniques in machine learning. First, vast amounts of data, i.e., “Big Data,” are available for systems to process. Second, advanced ... » read more

Power/Performance Bits: June 2
By Jesse Allen - 02 Jun, 2020 - Comments: 0

Neuromorphic memristor Researchers at the University of Massachusetts Amherst used protein nanowires to create neuromorphic memristors capable of running at extremely low voltage. A challenge to neuromorphic computing is mimicking the low voltage at which the brain operates: it sends signals between neurons at around 80 millivolts. Jun Yao, an electrical and computer engineering researcher at ... » read more

Power/Performance Bits: May 19
By Jesse Allen - 19 May, 2020 - Comments: 0

Neuromorphic magnetic nanowires Researchers from the University of Texas at Austin, University of Texas at Dallas, and Sandia National Laboratory propose a neuromorphic computing method using magnetic components. The team says this approach can cut the energy cost of training neural networks. "Right now, the methods for training your neural networks are very energy-intensive," said Jean Ann... » read more

Spiking Neural Networks: Research Projects or Commercial Products?
By Bryon Moyer - 18 May, 2020 - Comments: 2

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

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