A survey on efficient convolutional neural networks and hardware acceleration
Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …
performance in academia and industry. The learning capability of convolutional neural …
Neural architecture search survey: A hardware perspective
KT Chitty-Venkata, AK Somani - ACM Computing Surveys, 2022 - dl.acm.org
We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …
Deep learning: As the new frontier in high-throughput plant phenotyping
S Arya, KS Sandhu, J Singh, S Kumar - Euphytica, 2022 - Springer
With climate change and ever-increasing population growth, the pace of varietal
development needs to be accelerated in order to feed a population of 10 billion by 2050 …
development needs to be accelerated in order to feed a population of 10 billion by 2050 …
Confuciux: Autonomous hardware resource assignment for dnn accelerators using reinforcement learning
DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs
during the DNN computations to reduce data movement from DRAM to the chip. The reuse is …
during the DNN computations to reduce data movement from DRAM to the chip. The reuse is …
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individual search methods have been replaced by weight-sharing search methods for higher …
individual search methods have been replaced by weight-sharing search methods for higher …
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 …
A full-stack search technique for domain optimized deep learning accelerators
The rapidly-changing deep learning landscape presents a unique opportunity for building
inference accelerators optimized for specific datacenter-scale workloads. We propose Full …
inference accelerators optimized for specific datacenter-scale workloads. We propose Full …
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 …
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 …