AI/ML algorithms and applications in VLSI design and technology
An evident challenge ahead for the integrated circuit (IC) industry is the investigation and
development of methods to reduce the design complexity ensuing from growing process …
development of methods to reduce the design complexity ensuing from growing process …
Combining SMOTE sampling and machine learning for forecasting wheat yields in France
This paper describes a method of predicting wheat yields based on machine learning, which
accurately determines the value of wheat yield losses in France. Obtaining reliable value …
accurately determines the value of wheat yield losses in France. Obtaining reliable value …
Run-time mapping of spiking neural networks to neuromorphic hardware
Neuromorphic architectures implement biological neurons and synapses to execute
machine learning algorithms with spiking neurons and bio-inspired learning algorithms …
machine learning algorithms with spiking neurons and bio-inspired learning algorithms …
Bring it on: Kinetic energy harvesting to spark machine learning computations in iots
S Shukla, SMP Dinakarrao - 2024 25th International …, 2024 - ieeexplore.ieee.org
The widespread adoption of Internet of Things (IoTs) and edge computing devices has made
them an integral part of our daily lives. The popularity of these devices has surged …
them an integral part of our daily lives. The popularity of these devices has surged …
FPGA-based convolutional neural network architecture with reduced parameter requirements
The success of deep learning has fast paced the evolution of current technology at
unprecedented rate. In particular, deep convolutional neural networks (CNNs) has gained a …
unprecedented rate. In particular, deep convolutional neural networks (CNNs) has gained a …
Approximate computing methods for embedded machine learning
Embedding Machine Learning enables integrating intelligence in recent application
domains such as Internet of Things, portable healthcare systems, and wearable devices …
domains such as Internet of Things, portable healthcare systems, and wearable devices …
X-DNNs: Systematic cross-layer approximations for energy-efficient deep neural networks
Growing interest towards the development of smart Cyber Physical Systems (CPS) and
Internet of Things (IoT) has motivated the researchers to explore the suitability of carrying out …
Internet of Things (IoT) has motivated the researchers to explore the suitability of carrying out …
Design and evaluation of a new machine learning framework for IoT and embedded devices
G Cornetta, A Touhafi - Electronics, 2021 - mdpi.com
Low-cost, high-performance embedded devices are proliferating and a plethora of new
platforms are available on the market. Some of them either have embedded GPUs or the …
platforms are available on the market. Some of them either have embedded GPUs or the …
CAxCNN: Towards the use of canonic sign digit based approximation for hardware-friendly convolutional neural networks
The design of hardware-friendly architectures with low computational overhead is desirable
for low latency realization of CNN on resource-constrained embedded platforms. In this …
for low latency realization of CNN on resource-constrained embedded platforms. In this …
PruNet: Class-blind pruning method for deep neural networks
DNNs are highly memory and computationally intensive, due to which they are unfeasible to
deploy in real time or mobile applications, where power and memory resources are scarce …
deploy in real time or mobile applications, where power and memory resources are scarce …