Automatic design of machine learning via evolutionary computation: A survey
Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …
knowledge from data, has been widely applied to practical applications, such as …
Co-exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks
Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating
platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units …
platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units …
A survey on evolutionary construction of deep neural networks
Automated construction of deep neural networks (DNNs) has become a research hot spot
nowadays because DNN's performance is heavily influenced by its architecture and …
nowadays because DNN's performance is heavily influenced by its architecture and …
Standing on the shoulders of giants: Hardware and neural architecture co-search with hot start
Hardware and neural architecture co-search that automatically generates artificial
intelligence (AI) solutions from a given dataset are promising to promote AI democratization; …
intelligence (AI) solutions from a given dataset are promising to promote AI democratization; …
Device-circuit-architecture co-exploration for computing-in-memory neural accelerators
Co-exploration of neural architectures and hardware design is promising due to its capability
to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the …
to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the …
Hao: Hardware-aware neural architecture optimization for efficient inference
Automatic algorithm-hardware co-design for DNN has shown great success in improving the
performance of DNNs on FPGAs. However, this process remains challenging due to the …
performance of DNNs on FPGAs. However, this process remains challenging due to the …
Enabling on-device cnn training by self-supervised instance filtering and error map pruning
This work aims to enable on-device training of convolutional neural networks (CNNs) by
reducing the computation cost at training time. CNN models are usually trained on high …
reducing the computation cost at training time. CNN models are usually trained on high …
Hardware design and the competency awareness of a neural network
The ability to estimate the uncertainty of predictions made by a neural network is essential
when applying neural networks to tasks such as medical diagnosis and autonomous …
when applying neural networks to tasks such as medical diagnosis and autonomous …
Can noise on qubits be learned in quantum neural network? a case study on quantumflow
In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal
with the high noise level existing in physical quantum bits (qubits). Quantum error correction …
with the high noise level existing in physical quantum bits (qubits). Quantum error correction …
Exploration of quantum neural architecture by mixing quantum neuron designs
With the constant increase of the number of quantum bits (qubits) in the actual quantum
computers, implementing and accelerating the prevalent deep learning on quantum …
computers, implementing and accelerating the prevalent deep learning on quantum …