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 …
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 …
NAX: neural architecture and memristive xbar based accelerator co-design
Neural Architecture Search (NAS) has provided the ability to design efficient deep neural
network (DNN) catered towards different hardwares like GPUs, CPUs etc. However …
network (DNN) catered towards different hardwares like GPUs, CPUs etc. However …
Algorithm and hardware co-design for reconfigurable cnn accelerator
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have
demonstrated their potential in automatically designing neural architectures and hardware …
demonstrated their potential in automatically designing neural architectures and hardware …
Hardware accelerator and neural network co-optimization for ultra-low-power audio processing devices
C Gerum, A Frischknecht, T Hald, PP Bernardo… - arXiv preprint arXiv …, 2022 - arxiv.org
The increasing spread of artificial neural networks does not stop at ultralow-power edge
devices. However, these very often have high computational demand and require …
devices. However, these very often have high computational demand and require …
Performance modeling of computer vision-based cnn on edge gpus
Convolutional Neural Networks (CNNs) are currently widely used in various fields,
particularly for computer vision applications. Edge platforms have drawn tremendous …
particularly for computer vision applications. Edge platforms have drawn tremendous …
A framework for neural network architecture and compile co-optimization
The efficiency of deep neural network (DNN) solutions on real hardware devices are mainly
decided by the DNN architecture and the compiler-level scheduling strategy on the …
decided by the DNN architecture and the compiler-level scheduling strategy on the …
Hardware-aware partitioning of convolutional neural network inference for embedded ai applications
F Kreß, J Hoefer, T Hotfilter, I Walter… - … Computing in Sensor …, 2022 - ieeexplore.ieee.org
Embedded image processing applications like multicamera-based object detection or
semantic segmentation are often based on Convolutional Neural Networks (CNNs) to …
semantic segmentation are often based on Convolutional Neural Networks (CNNs) to …