Dnn-chip predictor: An analytical performance predictor for dnn accelerators with various dataflows and hardware architectures
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously
increased demand for DNN accelerators. However, designing DNN accelerators is non …
increased demand for DNN accelerators. However, designing DNN accelerators is non …
From cnn to dnn hardware accelerators: A survey on design, exploration, simulation, and frameworks
Over the past decade, a massive proliferation of machine learning algorithms has emerged,
from applications for surveillance to self-driving cars. The turning point occurred with the …
from applications for surveillance to self-driving cars. The turning point occurred with the …
An automotive case study on the limits of approximation for object detection
The accuracy of camera-based object detection (CBOD) built upon deep learning is often
evaluated against the real objects in frames only. However, such simplistic evaluation …
evaluated against the real objects in frames only. However, such simplistic evaluation …
A fast, accurate, and comprehensive PPA estimation of convolutional hardware accelerators
LR Juracy, A de Morais Amory… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Convolutional Neural Networks (CNN) are widely adopted for Machine Learning (ML) tasks,
such as classification and computer vision. GPUs became the reference platforms for both …
such as classification and computer vision. GPUs became the reference platforms for both …
[LLIBRE][B] AI Computing Systems: An Application Driven Perspective
AI Computing Systems: An Application Driven Perspective adopts the principle of"
application-driven, full-stack penetration" and uses the specific intelligent application of" …
application-driven, full-stack penetration" and uses the specific intelligent application of" …
A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures
In recent years, the field of Deep Learning has seen many disruptive and impactful
advancements. Given the increasing complexity of deep neural networks, the need for …
advancements. Given the increasing complexity of deep neural networks, the need for …
Blackthorn: latency estimation framework for CNNs on embedded Nvidia platforms
With more powerful yet efficient embedded devices and accelerators being available for
Deep Neural Networks (DNN), machine learning is becoming an integral part of edge …
Deep Neural Networks (DNN), machine learning is becoming an integral part of edge …
Annette: Accurate neural network execution time estimation with stacked models
M Wess, M Ivanov, C Unger, A Nookala, A Wendt… - IEEE …, 2020 - ieeexplore.ieee.org
With new accelerator hardware for Deep Neural Networks (DNNs), the computing power for
Artificial Intelligence (AI) applications has increased rapidly. However, as DNN algorithms …
Artificial Intelligence (AI) applications has increased rapidly. However, as DNN algorithms …
DASM: Data-streaming-based computing in nonvolatile memory architecture for embedded system
Emerging nonvolatile memories (NVMs), including resistive RAM (RRAM), phase-change
memory (PCM), and magnetic RAM (MRAM), have opened up new pathways for Computing …
memory (PCM), and magnetic RAM (MRAM), have opened up new pathways for Computing …
Data scheduling and placement in deep learning accelerator
Deep neural networks (DNNs) have been employed to different devices as a popular
machine learning algorithm (ML) owing to deploy the Internet of Things (IoT), data mining in …
machine learning algorithm (ML) owing to deploy the Internet of Things (IoT), data mining in …