相关论文: High-speed ionic synaptic memory based on two-dime…
Traditional computation based on von Neumann architecture is limited by the time and energy consumption due to data transfer between the storage and the processing units. The von Neumann architecture is also inefficient in solving…
Spiking neural networks (SNNs) are being explored in an attempt to mimic brain's capability to learn and recognize at low power. Crossbar architecture with highly scalable Resistive RAM or RRAM array serving as synaptic weights and neuronal…
Edge computing systems struggle to efficiently manage multiple concurrent deep neural network (DNN) workloads while meeting strict latency requirements, minimizing power consumption, and maintaining environmental sustainability. This paper…
Deployment of deep neural networks in resource-constrained embedded systems requires innovative algorithmic solutions to facilitate their energy and memory efficiency. To further ensure the reliability of these systems against malicious…
The spectrum of two-dimensional (2D) materials beyond graphene offers a remarkable platform to study new phenomena in condensed matter physics. Among these materials, layered hexagonal boron nitride (hBN), with its wide bandgap energy…
Resistance-change random access memory (RRAM) devices are nanoscale metal-insulator-metal structures that can store information in their resistance states, namely the high resistance (HRS) and low resistance (LRS) states. They are a…
The rapid surge in data generated by Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) applications demands ultra-fast, scalable, and energy-efficient hardware, as traditional von Neumann architectures face…
Neural networks are an increasingly attractive algorithm for natural language processing and pattern recognition. Deep networks with >50M parameters are made possible by modern GPU clusters operating at <50 pJ per op and more recently,…
MXenes are a class of 2D/layered materials which are highly conductive, hydrophilic, have a large electrochemical surface area and are easily processible into electrodes for energy applications. Since the discovery of MXenes over ten years…
Two-dimensional (2D) transition metal nitrides (TMNs) are new members in the 2D materials family with a wide range of applications. Particularly, highly crystalline and large area thin films of TMNs are potentially promising for…
Nonvolatile phase change random access memory (PCRAM) is regarded as one of promising candidates for emerging mass storage in the era of Big Data. However, relatively high programming energy hurdles the further reduction of power…
Neuromorphic architectures, which incorporate parallel and in-memory processing, are crucial for accelerating artificial neural network (ANN) computations. This work presents a novel memristor-based multi-layer neural network (memristive…
Resistive switching memories allow electrical control of the conductivity of a material, by inducing a high resistance (OFF) or a low resistance (ON) state, using electrochemical and ion transport processes. As alternative to high…
Redox-based resistive switching devices (ReRAM) are an emerging class of non-volatile storage elements suited for nanoscale memory applications. In terms of logic operations, ReRAM devices were suggested to be used as programmable…
Substituting load-bearing tissues requires hydrogels with rapid processability, excellent mechanical strength and fatigue resistance. Conventional homogeneously polymerized hydrogels with short-chains/excessive branching exhibit low…
Owing to the data explosion and rapid development of artificial intelligence (AI), particularly deep neural networks (DNNs), the ever-increasing demand for large-scale matrix-vector multiplication has become one of the major issues in…
Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory. This precludes the implementation of CNNs in low-power embedded systems. Recent research shows CNNs sustain extreme quantization, binarizing their weights…
Ternary content addressable memories (TCAMs) are useful for certain computing tasks since they allow us to compare a search query with a whole dataset stored in the memory array. They can also unlock unique advantages for cryogenic…
Titanium nitride halides, TiNX (X = F, Cl, Br, I) in the {\alpha}-phase (orthorhombic) are exciting quasi two-dimensional (2D) electronic systems exhibiting a fascinating series of electronic ground states under different conditions.…
Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more…