Neuromemristive circuits for edge computing: A review
O Krestinskaya, AP James… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
The volume, veracity, variability, and velocity of data produced from the ever increasing
network of sensors connected to Internet pose challenges for power management …
network of sensors connected to Internet pose challenges for power management …
A survey of neuromorphic computing and neural networks in hardware
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …
and models that contrast the pervasive von Neumann computer architecture. This …
[HTML][HTML] Analog architectures for neural network acceleration based on non-volatile memory
Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …
have the potential to overcome the major bottlenecks faced by digital hardware for data …
Low-power linear computation using nonlinear ferroelectric tunnel junction memristors
Analogue in-memory computing using memristors could alleviate the performance
constraints imposed by digital von Neumann systems in data-intensive tasks. Conventional …
constraints imposed by digital von Neumann systems in data-intensive tasks. Conventional …
Memristive LSTM network for sentiment analysis
This paper presents a complete solution for the hardware design of a memristor-based long
short-term memory (MLSTM) network. Throughout the design process, we fully consider the …
short-term memory (MLSTM) network. Throughout the design process, we fully consider the …
Learning in memristive neural network architectures using analog backpropagation circuits
O Krestinskaya, KN Salama… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The on-chip implementation of learning algorithms would speed up the training of neural
networks in crossbar arrays. The circuit level design and implementation of a back …
networks in crossbar arrays. The circuit level design and implementation of a back …
Challenges and opportunities: From near-memory computing to in-memory computing
The confluence of the recent advances in technology and the ever-growing demand for
large-scale data analytics created a renewed interest in a decades-old concept, processing …
large-scale data analytics created a renewed interest in a decades-old concept, processing …
A self-rectifying TaOy/nanoporous TaOx memristor synaptic array for learning and energy-efficient neuromorphic systems
The human brain intrinsically operates with a large number of synapses, more than 1015.
Therefore, one of the most critical requirements for constructing artificial neural networks …
Therefore, one of the most critical requirements for constructing artificial neural networks …
RRAM-based analog approximate computing
Approximate computing is a promising design paradigm for better performance and power
efficiency. In this paper, we propose a power efficient framework for analog approximate …
efficiency. In this paper, we propose a power efficient framework for analog approximate …
A brain-inspired in-memory computing system for neuronal communication via memristive circuits
Brain-inspired approaches can efficiently analyze the activities of biological neural networks
and solve computationally hard problems with energy efficiencies unattainable with von …
and solve computationally hard problems with energy efficiencies unattainable with von …