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pro vyhledávání: '"Ankit, Aayush"'
In-Memory Computing (IMC) hardware using Memristive Crossbar Arrays (MCAs) are gaining popularity to accelerate Deep Neural Networks (DNNs) since it alleviates the "memory wall" problem associated with von-Neumann architecture. The hardware efficienc
Externí odkaz:
http://arxiv.org/abs/2106.12125
The analog nature of computing in Memristive crossbars poses significant issues due to various non-idealities such as: parasitic resistances, non-linear I-V characteristics of the device etc. The non-idealities can have a detrimental impact on the fu
Externí odkaz:
http://arxiv.org/abs/2003.06902
Humans learn adaptively and efficiently throughout their lives. However, incrementally learning tasks causes artificial neural networks to overwrite relevant information learned about older tasks, resulting in 'Catastrophic Forgetting'. Efforts to ov
Externí odkaz:
http://arxiv.org/abs/2001.08650
Autor:
Ankit, Aayush, Hajj, Izzat El, Chalamalasetti, Sai Rahul, Agarwal, Sapan, Marinella, Matthew, Foltin, Martin, Strachan, John Paul, Milojicic, Dejan, Hwu, Wen-mei, Roy, Kaushik
The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Numerous special-purpose architectures have been proposed to accelerate training: both digi
Externí odkaz:
http://arxiv.org/abs/1912.11516
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. O
Externí odkaz:
http://arxiv.org/abs/1906.08167
Publikováno v:
Nature Machine Intelligence, 2, 43-55 (2020)
The `Internet of Things' has brought increased demand for AI-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles. Quantization is a powerful tool to address the growing computational cost of such app
Externí odkaz:
http://arxiv.org/abs/1906.01493
The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. Although extre
Externí odkaz:
http://arxiv.org/abs/1902.00460
Autor:
Ankit, Aayush, Hajj, Izzat El, Chalamalasetti, Sai Rahul, Ndu, Geoffrey, Foltin, Martin, Williams, R. Stanley, Faraboschi, Paolo, Hwu, Wen-mei, Strachan, John Paul, Roy, Kaushik, Milojicic, Dejan S
Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have been shown to be effective in special-purpose accelerators for a limited
Externí odkaz:
http://arxiv.org/abs/1901.10351
Autor:
Agrawal, Amogh, Jaiswal, Akhilesh, Roy, Deboleena, Han, Bing, Srinivasan, Gopalakrishnan, Ankit, Aayush, Roy, Kaushik
Deep neural networks are a biologically-inspired class of algorithms that have recently demonstrated state-of-the-art accuracies involving large-scale classification and recognition tasks. Indeed, a major landmark that enables efficient hardware acce
Externí odkaz:
http://arxiv.org/abs/1807.00343
Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational tim
Externí odkaz:
http://arxiv.org/abs/1712.02719