Search Results for author:
Found 9 papers, 2 papers with code
VVTEAM: A Compact Behavioral Model for Volatile Memristors
Volatile memristors have recently gained popularity as promising devices for neuromorphic circuits, capable of mimicking the leaky function of neurons and offering advantages over capacitor-based circuits in terms of power dissipation and area.
Roadmap to Neuromorphic Computing with Emerging Technologies
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement.
A Pipelined Memristive Neural Network Analog-to-Digital Converter
With the advent of high-speed, high-precision, and low-power mixed-signal systems, there is an ever-growing demand for accurate, fast, and energy-efficient analog-to-digital (ADCs) and digital-to-analog converters (DACs).
ClaPIM: Scalable Sequence CLAssification using Processing-In-Memory
Specifically, we propose a custom filtering technique that drastically narrows the search space and a search approach that facilitates approximate string matching through a distance function.
FiltPIM: In-Memory Filter for DNA Sequencing
Pre-alignment filters substantially reduce computation complexity by filtering potential alignment locations.
C-AND: Mixed Writing Scheme for Disturb Reduction in 1T Ferroelectric FET Memory
In this paper, we propose a memory architecture named crossed-AND (C-AND), in which each storage cell consists of a single ferroelectric transistor.
Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices
Nevertheless, the implementation of the VMM needs complex peripheral circuits and the complexity further increases since non-idealities of memristive devices prevent precise conductance tuning (especially for the online training) and largely degrade the performance of the deep neural networks (DNNs).
Training of Quantized Deep Neural Networks using a Magnetic Tunnel Junction-Based Synapse
A recent example is the GXNOR framework for stochastic training of ternary (TNN) and binary (BNN) neural networks.
A Systematic Approach to Blocking Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations.
