Weight-sharing neural architecture search: A battle to shrink the optimization gap
Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …
individual search methods have been replaced by weight-sharing search methods for higher …
Deep neural network–based enhancement for image and video streaming systems: A survey and future directions
Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual
apps spanning from on-demand movies and 360° videos to video-conferencing and live …
apps spanning from on-demand movies and 360° videos to video-conferencing and live …
Hardware acceleration of sparse and irregular tensor computations of ml models: A survey and insights
Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …
processing these computational-and memory-intensive applications, tensors of these …
A survey on the optimization of neural network accelerators for micro-ai on-device inference
Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …
Neural architecture search and hardware accelerator co-search: A survey
L Sekanina - IEEE access, 2021 - ieeexplore.ieee.org
Deep neural networks (DNN) are now dominating in the most challenging applications of
machine learning. As DNNs can have complex architectures with millions of trainable …
machine learning. As DNNs can have complex architectures with millions of trainable …
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 …
Dance: Differentiable accelerator/network co-exploration
This work presents DANCE, a differentiable approach towards the co-exploration of
hardware accelerator and network architecture design. At the heart of DANCE is a …
hardware accelerator and network architecture design. At the heart of DANCE is a …
Intermittent-aware neural architecture search
The increasing paradigm shift towards i ntermittent computing has made it possible to
intermittently execute d eep neural network (DNN) inference on edge devices powered by …
intermittently execute d eep neural network (DNN) inference on edge devices powered by …
G-CoS: GNN-accelerator co-search towards both better accuracy and efficiency
Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA) method for
graph-based learning tasks. However, it still remains prohibitively challenging to inference …
graph-based learning tasks. However, it still remains prohibitively challenging to inference …
Automated HW/SW co-design for edge AI: State, challenges and steps ahead
O Bringmann, W Ecker, I Feldner… - Proceedings of the …, 2021 - dl.acm.org
Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …