A comprehensive survey on hardware-aware neural architecture search
Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …
techniques have been fundamental to automate and speed up the time consuming and error …
Multiply accumulate operations in memristor crossbar arrays for analog computing
J Chen, J Li, Y Li, X Miao - Journal of Semiconductors, 2021 - iopscience.iop.org
Memristors are now becoming a prominent candidate to serve as the building blocks of non-
von Neumann in-memory computing architectures. By mapping analog numerical matrices …
von Neumann in-memory computing architectures. By mapping analog numerical matrices …
Programmable black phosphorus image sensor for broadband optoelectronic edge computing
Image sensors with internal computing capability enable in-sensor computing that can
significantly reduce the communication latency and power consumption for machine vision …
significantly reduce the communication latency and power consumption for machine vision …
[图书][B] Efficient processing of deep neural networks
This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …
Mixed-signal computing for deep neural network inference
B Murmann - IEEE Transactions on Very Large Scale …, 2020 - ieeexplore.ieee.org
Modern deep neural networks (DNNs) require billions of multiply-accumulate operations per
inference. Given that these computations demand relatively low precision, it is feasible to …
inference. Given that these computations demand relatively low precision, it is feasible to …
Review of ASIC accelerators for deep neural network
Deep neural networks (DNNs) have become an essential tool in artificial intelligence, with a
wide range of applications such as computer vision, medical diagnosis, security, robotics …
wide range of applications such as computer vision, medical diagnosis, security, robotics …
How to evaluate deep neural network processors: Tops/w (alone) considered harmful
A significant amount of specialized hardware has been developed for processing deep
neural networks (DNNs) in both academia and industry. This article aims to highlight the key …
neural networks (DNNs) in both academia and industry. This article aims to highlight the key …
Libraries of approximate circuits: Automated design and application in CNN accelerators
Libraries of approximate circuits are composed of fully characterized digital circuits that can
be used as building blocks of energy-efficient implementations of hardware accelerators …
be used as building blocks of energy-efficient implementations of hardware accelerators …
Tinyvers: A tiny versatile system-on-chip with state-retentive eMRAM for ML inference at the extreme edge
Extreme edge devices or Internet-of-Things (IoT) nodes require both ultra-low power (ULP)
always-on (AON) processing as well as the ability to do on-demand sampling and …
always-on (AON) processing as well as the ability to do on-demand sampling and …
A precision-scalable deep neural network accelerator with activation sparsity exploitation
W Li, A Hu, N Xu, G He - IEEE Transactions on Computer-Aided …, 2023 - ieeexplore.ieee.org
To meet the demand in a wide range of practical applications, precision-scalable deep
neural network (DNN) accelerators are becoming an unavoidable trend. On the other hand …
neural network (DNN) accelerators are becoming an unavoidable trend. On the other hand …