Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers
The vast majority of processors in the world are actually microcontroller units (MCUs), which
find widespread use performing simple control tasks in applications ranging from …
find widespread use performing simple control tasks in applications ranging from …
Entropy-driven mixed-precision quantization for deep network design
Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is
challenging due to the limited computational resources, such as limited SRAM memory and …
challenging due to the limited computational resources, such as limited SRAM memory and …
Leaky nets: Recovering embedded neural network models and inputs through simple power and timing side-channels—Attacks and defenses
S Maji, U Banerjee… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
With the recent advancements in machine learning theory, many commercial embedded
microprocessors use neural network (NN) models for a variety of signal processing …
microprocessors use neural network (NN) models for a variety of signal processing …
RNNPool: Efficient non-linear pooling for RAM constrained inference
Abstract Standard Convolutional Neural Networks (CNNs) designed for computer vision
tasks tend to have large intermediate activation maps. These require large working memory …
tasks tend to have large intermediate activation maps. These require large working memory …
A threshold implementation-based neural network accelerator with power and electromagnetic side-channel countermeasures
S Maji, U Banerjee, SH Fuller… - IEEE Journal of Solid …, 2022 - ieeexplore.ieee.org
With the recent advancements in machine learning (ML) theory, a lot of energy-efficient
neural network (NN) accelerators have been developed. However, their associated side …
neural network (NN) accelerators have been developed. However, their associated side …
Neural network for low-memory IoT devices and MNIST image recognition using kernels based on logistic map
A Velichko - Electronics, 2020 - mdpi.com
This study presents a neural network which uses filters based on logistic mapping
(LogNNet). LogNNet has a feedforward network structure, but possesses the properties of …
(LogNNet). LogNNet has a feedforward network structure, but possesses the properties of …
Resot: Resource-efficient oblique trees for neural signal classification
Classifiers that can be implemented on chip with minimal computational and memory
resources are essential for edge computing in emerging applications such as medical and …
resources are essential for edge computing in emerging applications such as medical and …
Rethinking generalization in american sign language prediction for edge devices with extremely low memory footprint
Due to the boom in technical compute in the last few years, the world has seen massive
advances in artificially intelligent systems solving diverse real-world problems. But a major …
advances in artificially intelligent systems solving diverse real-world problems. But a major …
A method for medical data analysis using the LogNNet for clinical decision support systems and edge computing in healthcare
A Velichko - Sensors, 2021 - mdpi.com
Edge computing is a fast-growing and much needed technology in healthcare. The problem
of implementing artificial intelligence on edge devices is the complexity and high resource …
of implementing artificial intelligence on edge devices is the complexity and high resource …
An energy-efficient neural network accelerator with improved resilience against fault attacks
Embedded neural network (NN) implementations are vulnerable to misclassification under
fault attacks (FAs). Clock glitching and injecting strong electromagnetic (EM) pulses are two …
fault attacks (FAs). Clock glitching and injecting strong electromagnetic (EM) pulses are two …