Related papers: InMRAM: Introductory course on Magnetic Random Acc…
In this innovative practice work-in-progress paper, we compare two different methods to teach machine learning concepts to undergraduate students in Electrical Engineering. While machine learning is now being offered as a senior-level…
Resistive memory technologies promise to be a key component in unlocking the next generation of intelligent in-memory computing systems that can act and learn locally at the edge. However, current approaches to in-memory machine learning…
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for…
Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive applications, and SRAM technology scaling and leakage power limits the efficiency of embedded memories. Future on-chip storage will need higher density…
Pr1-xCaxMnO3 (PCMO) based resistance random access memory (RRAM) is attractive in large scale memory and neuromorphic applications as it is non-filamentary, area scalable and has multiple resistance states along with excellent endurance and…
There is increasing demand to bring machine learning capabilities to low power devices. By integrating the computational power of machine learning with the deployment capabilities of low power devices, a number of new applications become…
In this paper, we develop an in-memory analog computing (IMAC) architecture realizing both synaptic behavior and activation functions within non-volatile memory arrays. Spin-orbit torque magnetoresistive random-access memory (SOT-MRAM)…
The demand for low-dissipation nanoscale memory devices is as strong as ever. As Moore's Law is staggering, and the demand for a low-power-consuming supercomputer is high, the goal of making information processing circuits out of…
To optimize the design of STT-MRAM (spin-transfer torque magnetic random access memory), it is necessary to be able to predict switching (error) rates. For small elements, this can be done using a single-macrospin theory since the element…
Antiferromagnetic spintronics is an emerging research field whose focus is on the electrical and optical control of the antiferromagnetic order parameter and its utility in information technology devices. An example of recently discovered…
Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase Change Materials (PCMs) have been showed…
The authors proposed and computationally analyzed nonvolatile static random access memory (NV-SRAM) architecture using metal-oxide-semiconductor field-effect transistor (MOSFET) type of spin-transistors referred to as pseudo-spin-MOSFET…
Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by…
Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of 1H-MRS is the low Signal-Noise Ratio (SNR). To improve the SNR, a typical approach is to perform Signal Averaging (SA) with M…
In-memory computing (IMC) has gained significant attention recently as it attempts to reduce the impact of memory bottlenecks. Numerous schemes for digital IMC are presented in the literature, focusing on logic operations. Often, an…
Objective: To allow efficient learning using the Recurrent Inference Machine (RIM) for image reconstruction whereas not being strictly dependent on the training data distribution so that unseen modalities and pathologies are still…
Deep learning training involves a large number of operations, which are dominated by high dimensionality Matrix-Vector Multiplies (MVMs). This has motivated hardware accelerators to enhance compute efficiency, but where data movement and…
Spintronics has attracted wide attention by promising novel functionalities derived from both the electron charge and spin. While branching into new areas and creating new themes over the past years, the principal goals remain the spin and…
As nanoelectronics approaches the nanometer scale, a massive effort is underway to identify the next scalable logic technology beyond Complementary Metal Oxide Semiconductor (CMOS) computing. Such computing technology needs to improve…
Molecular spintronics is recognized to as an attractive new research direction in a field of spintronics, following to metallic spintronics and inorganic semiconductor spintronics, and attracts many people in recent decades. The purpose of…