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pro vyhledávání: '"Hu, Xiaobo"'
Compute-in-memory (CIM) accelerators using non-volatile memory (NVM) devices offer promising solutions for energy-efficient and low-latency Deep Neural Network (DNN) inference execution. However, practical deployment is often hindered by the challeng
Externí odkaz:
http://arxiv.org/abs/2406.06544
As the reliance on secure memory environments permeates across applications, memory encryption is used to ensure memory security. However, most effective encryption schemes, such as the widely used AES-CTR, inherently introduce extra overheads, inclu
Externí odkaz:
http://arxiv.org/abs/2402.15824
Architectures that incorporate Computing-in-Memory (CiM) using emerging non-volatile memory (NVM) devices have become strong contenders for deep neural network (DNN) acceleration due to their impressive energy efficiency. Yet, a significant challenge
Externí odkaz:
http://arxiv.org/abs/2401.05357
Emerging non-volatile memory (NVM)-based Computing-in-Memory (CiM) architectures show substantial promise in accelerating deep neural networks (DNNs) due to their exceptional energy efficiency. However, NVM devices are prone to device variations. Con
Externí odkaz:
http://arxiv.org/abs/2312.06137
Given an object of interest, visual navigation aims to reach the object's location based on a sequence of partial observations. To this end, an agent needs to 1) learn a piece of certain knowledge about the relations of object categories in the world
Externí odkaz:
http://arxiv.org/abs/2312.03327
Visual reinforcement learning has proven effective in solving control tasks with high-dimensional observations. However, extracting reliable and generalizable representations from vision-based observations remains a central challenge. Inspired by the
Externí odkaz:
http://arxiv.org/abs/2312.01915
In this work, we present ODHD, an algorithm for outlier detection based on hyperdimensional computing (HDC), a non-classical learning paradigm. Along with the HDC-based algorithm, we propose IM-ODHD, a computing-in-memory (CiM) implementation based o
Externí odkaz:
http://arxiv.org/abs/2311.17852
Autor:
Farzaneh, Hamid, de Lima, João Paulo Cardoso, Li, Mengyuan, Khan, Asif Ali, Hu, Xiaobo Sharon, Castrillon, Jeronimo
Machine learning and data analytics applications increasingly suffer from the high latency and energy consumption of conventional von Neumann architectures. Recently, several in-memory and near-memory systems have been proposed to remove this von Neu
Externí odkaz:
http://arxiv.org/abs/2309.06418
Autor:
Geng, Haoran, Mo, Jianqiao, Reis, Dayane, Takeshita, Jonathan, Jung, Taeho, Reagen, Brandon, Niemier, Michael, Hu, Xiaobo Sharon
Privacy has rapidly become a major concern/design consideration. Homomorphic Encryption (HE) and Garbled Circuits (GC) are privacy-preserving techniques that support computations on encrypted data. HE and GC can complement each other, as HE is more e
Externí odkaz:
http://arxiv.org/abs/2308.02648
Autor:
Aziz, Ahmedullah, Breyer, Evelyn T., Chen, An, Chen, Xiaoming, Datta, Suman, Gupta, Sumeet Kumar, Hoffmann, Michael, Hu, Xiaobo Sharon, Ionescu, Adrian, Jerry, Matthew, Mikolajick, Thomas, Mulaosmanovic, Halid, Ni, Kai, Niemier, Michael, O'Connor, Ian, Saha, Atanu, Slesazeck, Stefan, Thirumala, Sandeep Krishna, Yin, Xunzhao
In this paper, we consider devices, circuits, and systems comprised of transistors with integrated ferroelectrics. Said structures are actively being considered by various semiconductor manufacturers as they can address a large and unique design spac
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A76838
https://tud.qucosa.de/api/qucosa%3A76838/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A76838/attachment/ATT-0/