An energy-efficient digital ReRAM-crossbar-based CNN with bitwise parallelism
There is great attention to develop hardware accelerator with better energy efficiency, as
well as throughput, than GPUs for convolutional neural network (CNN). The existing …
well as throughput, than GPUs for convolutional neural network (CNN). The existing …
An energy-efficient nonvolatile in-memory computing architecture for extreme learning machine by domain-wall nanowire devices
The data-oriented applications have introduced increased demands on memory capacity
and bandwidth, which raises the need to rethink the architecture of the current computing …
and bandwidth, which raises the need to rethink the architecture of the current computing …
Distributed in-memory computing on binary RRAM crossbar
The recently emerging resistive random-access memory (RRAM) can provide nonvolatile
memory storage but also intrinsic computing for matrix-vector multiplication, which is ideal …
memory storage but also intrinsic computing for matrix-vector multiplication, which is ideal …
Asymmetrical training scheme of binary-memristor-crossbar-based neural networks for energy-efficient edge-computing nanoscale systems
For realizing neural networks with binary memristor crossbars, memristors should be
programmed by high-resistance state (HRS) and low-resistance state (LRS), according to …
programmed by high-resistance state (HRS) and low-resistance state (LRS), according to …
A highly parallel and energy efficient three-dimensional multilayer CMOS-RRAM accelerator for tensorized neural network
It is a grand challenge to develop highly parallel yet energy-efficient machine learning
hardware accelerator. This paper introduces a three-dimensional (3-D) multilayer …
hardware accelerator. This paper introduces a three-dimensional (3-D) multilayer …
Data-driven sampling matrix boolean optimization for energy-efficient biomedical signal acquisition by compressive sensing
Compressive sensing is widely used in biomedical applications, and the sampling matrix
plays a critical role on both quality and power consumption of signal acquisition. It projects a …
plays a critical role on both quality and power consumption of signal acquisition. It projects a …
Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory
Image processing in conventional logic-memory I/O-integrated systems will incur significant
communication congestion at memory I/Os for excessive big image data at exa-scale. This …
communication congestion at memory I/Os for excessive big image data at exa-scale. This …
Data backup optimization for nonvolatile SRAM in energy harvesting sensor nodes
Nonvolatile static random access memory (nvSRAM) has been widely investigated as a
promising on-chip memory architecture in energy harvesting sensor nodes, due to zero …
promising on-chip memory architecture in energy harvesting sensor nodes, due to zero …
Muller C-element exploiting programmable metallization cell for Bayesian inference
Decision-making via Bayesian inference is a prominent operation in several autonomous
applications, including robotics, brain-machine interactions, artificial intelligence (AI) agents …
applications, including robotics, brain-machine interactions, artificial intelligence (AI) agents …
Simulating the filament morphology in electrochemical metallization cells
Electrochemical metallization (ECM) cells are based on the principle of voltage controlled
formation or dissolution of a nanometer-thin metallic conductive filament (CF) between two …
formation or dissolution of a nanometer-thin metallic conductive filament (CF) between two …