新兴技术

Stateful logic is a promising processing-in-memory (PIM) paradigm to perform logic operations using emerging nonvolatile memory cells. While most stateful logic circuits to date focused on technologies such as resistive RAM, we propose two…

This article highlights quantum Internet computing as referring to distributed quantum computing over the quantum Internet, analogous to (classical) Internet computing involving (classical) distributed computing over the (classical)…

Convolutional neural networks (CNN) have become a ubiquitous algorithm with growing applications in mobile and edge settings. We describe a compute-in-memory (CIM) technique called FPIRM using Racetrack Memory (RM) to accelerate CNNs for…

The growth in data needs of modern applications has created significant challenges for modern systems leading a "memory wall." Spintronic Domain Wall Memory (DWM), related to Spin-Transfer Torque Memory (STT-MRAM), provides near-SRAM…

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new…

In this work, we experimentally demonstrate two key building blocks for realizing Binary/Ternary Neural Networks (BNNs/TNNs): (i) 130 nm CMOS based sigmoidal neurons and (ii) HfOx based multi-level (MLC) OxRAM-synaptic blocks. An optimized…

Emerging two terminal nanoscale memory devices, known as memristors, have over the past decade demonstrated great potential for implementing energy efficient neuro-inspired computing architectures. As a result, a wide-range of technologies…

A multiscale simulation method is developed to model a quantum dot (QD) array of germanium (Ge) holes for quantum computing. Guided by three-dimensional numerical quantum device simulations of QD structures, an analytical model of the…

We introduce a new method for inverse design of nanophotonic devices which guarantees that resulting designs satisfy strict length scale constraints - including minimum width and spacing constraints required by commercial semiconductor…

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann…

The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous ONN architectures are mainly designed for general matrix…

One of the crucial tasks in computer science is the processing time reduction of various data types, i.e., images, which is important for different fields -- from medicine and logistics to virtual shopping. Compared to classical computers,…

Analog/mixed-signal circuit design is one of the most complex and time-consuming stages in the whole chip design process. Due to various process, voltage, and temperature (PVT) variations from chip manufacturing, analog circuits inevitably…

This paper provides a comparison of current video content extraction tools with a focus on comparing commercial task-based machine learning services. Video intelligence (VIDINT) data has become a critical intelligence source in the past…

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions.…

Physical reservoir computing has recently been attracting attention for its ability to significantly reduce the computational resources required to process time-series data. However, the physical reservoirs that have been reported to date…

Emerging memristor computing systems have demonstrated great promise in improving the energy efficiency of neural network (NN) algorithms. The NN weights stored in memristor crossbars, however, may face potential theft attacks due to the…

Simultaneous wireless information and power transfer (SWIPT) is a remarkable technology to support both the data and the energy transfer in the era of Internet of Things (IoT). In this paper, we proposed a long-range optical wireless…

This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task…

In this work, we present a quantum circuit model for inferring gene regulatory networks (GRNs). The model is based on the idea of using qubit-qubit entanglement to simulate interactions between genes. We provide preliminary results that…