A survey on modeling and improving reliability of DNN algorithms and accelerators
S Mittal - Journal of Systems Architecture, 2020 - Elsevier
As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …
reliable operation has become crucial. Conventional resilience techniques fail to account for …
Integrated photonic tensor processing unit for a matrix multiply: a review
The explosion of artificial intelligence and machine-learning algorithms, connected to the
exponential growth of the exchanged data, is driving a search for novel application-specific …
exponential growth of the exchanged data, is driving a search for novel application-specific …
EDEN: Enabling energy-efficient, high-performance deep neural network inference using approximate DRAM
The effectiveness of deep neural networks (DNN) in vision, speech, and language
processing has prompted a tremendous demand for energy-efficient high-performance DNN …
processing has prompted a tremendous demand for energy-efficient high-performance DNN …
Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
Thundervolt: enabling aggressive voltage underscaling and timing error resilience for energy efficient deep learning accelerators
Hardware accelerators are being increasingly deployed to boost the performance and
energy efficiency of deep neural network (DNN) inference. In this paper we propose …
energy efficiency of deep neural network (DNN) inference. In this paper we propose …
A systematic literature review on hardware reliability assessment methods for deep neural networks
Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …
utilized in various applications due to their capability to learn how to solve complex …
Toward functional safety of systolic array-based deep learning hardware accelerators
High accuracy and ever-increasing computing power have made deep neural networks
(DNNs) the algorithm of choice for various machine learning, computer vision, and image …
(DNNs) the algorithm of choice for various machine learning, computer vision, and image …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
Fault-tolerant systolic array based accelerators for deep neural network execution
Editor's note: Systolic array is embracing its renaissance after being accepted by Google
TPU as the core computing architecture of machine learning acceleration. In this article, the …
TPU as the core computing architecture of machine learning acceleration. In this article, the …
On the resilience of rtl nn accelerators: Fault characterization and mitigation
Machine Learning (ML) is making a strong resurgence in tune with the massive generation
of unstructured data which in turn requires massive computational resources. Due to the …
of unstructured data which in turn requires massive computational resources. Due to the …