Related papers: Neuron inspired data encoding memristive multi-lev…
The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing…
Memory effects are ubiquitous in nature and the class of memory circuit elements - which includes memristors, memcapacitors and meminductors - shows great potential to understand and simulate the associated fundamental physical processes.…
We present a novel cryptography architecture based on memristor crossbar array, binary hypervectors, and neural network. Utilizing the stochastic and unclonable nature of memristor crossbar and error tolerance of binary hypervectors and…
Transformers have emerged as the backbone of large language models (LLMs). However, generation remains inefficient due to the need to store in memory a cache of key-value representations for past tokens, whose size scales linearly with the…
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum…
The precise programming of crossbar arrays of unit-cells is crucial for obtaining high matrix-vector-multiplication (MVM) accuracy in analog in-memory computing (AIMC) cores. We propose a radically different approach based on directly…
This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are…
Despite recent progress in memory augmented neural network (MANN) research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. Especially the content-based…
Physical-layer Network Coding (PNC) can significantly improve the throughput of two-way relay channels. An interesting variant of PNC is Analog Network Coding (ANC). Almost all ANC schemes proposed to date, however, operate in a symbol by…
We propose binary discrete parametric channel models for multi-level cell (MLC) flash memories that provide accurate ECC performance estimation by modeling the empirically observed error characteristics under program/erase (P/E) cycling…
Multimodal Large Language Models (MLLMs) have achieved remarkable performance by aligning pretrained visual representations with the linguistic knowledge embedded in Large Language Models (LLMs). However, existing approaches typically rely…
Compact models of memristors are essential for simulating large-scale neuromorphic systems, yet they often do not include description of complex dynamics like volatile relaxation and synaptic plasticity. We introduce a modular,…
Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to…
Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to store model parameters, which are programmed as resistance states. NVMs suffer from the read disturb issue, where the programmed resistance state drifts upon repeated…
We have designed, fabricated, and successfully tested a prototype mixed-signal, 28x28-binary-input, 10-output, 3-layer neuromorphic network ("MLP perceptron"). It is based on embedded nonvolatile floating-gate cell arrays redesigned from a…
This paper presents a memristor-based compute-in-memory hardware accelerator for on-chip training and inference, focusing on its accuracy and efficiency against device variations, conductance errors, and input noise. Utilizing realistic…
This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses. Design considerations and challenges such as requirements and design…
By exploiting the intrinsic random nature of nanoscale devices, Memristor Monte Carlo (MMC) is a promising enabler of edge learning systems. However, due to multiple algorithmic and device-level limitations, existing demonstrations have…
Dynamic reconfiguration of charge carriers in confined ion-channels under electrical stimulation produces memory effects, where the internal resistance depends on history of the electric field. Vermiculite nanofluidic devices harness this…
Construction and training principles have been proposed and tested for an artificial neural network based on metal-oxide thin-film nanostructures possessing bipolar resistive switching (memristive) effect. Experimental electronic circuit of…