A systematic literature review on binary neural networks
R Sayed, H Azmi, H Shawkey, AH Khalil… - IEEE Access, 2023 - ieeexplore.ieee.org
This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …
utilizes binary weights and activation function parameters to substitute the full-precision …
Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell
Memristor-based neural networks provide an exceptional energy-efficient platform for
artificial intelligence (AI), presenting the possibility of self-powered operation when paired …
artificial intelligence (AI), presenting the possibility of self-powered operation when paired …
Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware
Neuromorphic computing is becoming a popular approach for implementations of brain-
inspired machine learning tasks. As a paradigm for both hardware and algorithm design …
inspired machine learning tasks. As a paradigm for both hardware and algorithm design …
An efficient and robust semantic hashing framework for similar text search
Similar text search aims to find texts relevant to a given query from a database, which is
fundamental in many information retrieval applications, such as question search and …
fundamental in many information retrieval applications, such as question search and …
Svnet: Where so (3) equivariance meets binarization on point cloud representation
Efficiency and robustness are increasingly needed for applications on 3D point clouds, with
the ubiquitous use of edge devices in scenarios like autonomous driving and robotics, which …
the ubiquitous use of edge devices in scenarios like autonomous driving and robotics, which …
REDsec: Running encrypted discretized neural networks in seconds
Abstract Machine learning as a service (MLaaS) has risen to become a prominent
technology due to the large development time, amount of data, hardware costs, and level of …
technology due to the large development time, amount of data, hardware costs, and level of …
An in-memory computing architecture utilizing energy-efficient vgsot mram device
MR Sarkar, CY Yi - IEEE Transactions on Circuits and Systems …, 2024 - ieeexplore.ieee.org
This brief introduces a novel 1.57-Mb IMC architecture that utilizes emerging voltage-gated
spin-orbit torque magnetic random-access memory (VGSOT MRAM) device. Apart from …
spin-orbit torque magnetic random-access memory (VGSOT MRAM) device. Apart from …
Design of a 2-bit neural network quantizer for Laplacian source
Achieving real-time inference is one of the major issues in contemporary neural network
applications, as complex algorithms are frequently being deployed to mobile devices that …
applications, as complex algorithms are frequently being deployed to mobile devices that …
Whether the support region of three-bit uniform quantizer has a strong impact on post-training quantization for MNIST dataset?
Driven by the need for the compression of weights in neural networks (NNs), which is
especially beneficial for edge devices with a constrained resource, and by the need to utilize …
especially beneficial for edge devices with a constrained resource, and by the need to utilize …
Performance of Post‐Training Two‐Bits Uniform and Layer‐Wise Uniform Quantization for MNIST Dataset from the Perspective of Support Region Choice
This paper contributes to the goal of finding an efficient compression solution for post‐
training quantization from the perspective of support region choice under the framework of …
training quantization from the perspective of support region choice under the framework of …